1 Descriptive Statistics

Data in Table 1 is from House of Commons Library reports (Audickas, Hawkins, & Cracknell, 2017; Kelly, 2016). All women short lists were not used by Labour during the 2001 General Election.

Labour MPs and Intakes
General Election Total MPs Labour MPs Female Labour MPs Labour MPs Intake Intake Women Intake Short list Nominated Short list
1997 659 418 101 (24%) 177 64 (36%) 35 38
2001 659 412 95 (23%) 38 4 (11%) 0 0
2005 646 355 98 (28%) 40 26 (65%) 23 30
2010 650 258 81 (31%) 64 32 (50%) 28 63
2015 650 232 99 (43%) 49 31 (63%) 31 77
Number of Speeches and Words in Dataset
Gender Speeches Words
All 656,412 111,180,398
Female 148,702 26,231,034
Male 507,710 84,949,364
Conservatives
All 285,291 44,800,169
Female 48,768 7,363,031
Male 236,523 37,437,138
Labour
All 261,942 46,494,850
Female 84,569 15,897,929
Non-All Women Shortlists 28,695 5,422,776
All Women Shortlists 55,874 10,475,153
Male 177,373 30,596,921
Liberal Democrat
All 72,716 13,485,902
Female 7,552 1,503,459
Male 65,164 11,982,443
Other
All 36,463 6,399,477
Female 7,813 1,466,615
Male 28,650 4,932,862

2 Methodology

Previous research on gender differences in political speech patterns has focused on differences between male and female politicians (Yu, 2014) or on variations in Hilary Clinton’s speech patterns (Bligh, Merolla, Schroedel, & Gonzalez, 2010; Jones, 2016). This paper focuses on differences in speech patterns between female Labour MPs nominated through All Women Shortlists (AWS) and female Labour MPs nominated through open short lists. We examined differences in speaking styles using the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker, Boyd, Jordan, & Blackburn, 2015) and the spaCy (Honnibal & Montani, 2017) Parts-of-Speech (POS) tagger. We examined differences in the topics discussed by AWS and non-AWS MPs, using \({\chi}^2\) tests for individual words and for bigrams. We trained a Naive Bayes classifier to distinguish AWS and non-AWS speeches. We used structured topic models (STM) to identify the topics discussed by AWS and non-AWS MPs.

To account for the possible effects of age, parliamentary experience and cohort, and in order to compare women selected through all women short lists to women who were not (but who theoretically had the opportunity to contest all-women short lists), our analysis is been restricted only to Labour MPs first elected to the House of Commons in the 1997 General Election, up to but excluding the 2017 General Election. Comparisons between MPs of different parties are also restricted to MPs first elected in the 1997 General Election, and before the 2017 General Election. Speeches made by the Speaker, including Deputy Speakers, were also excluded. Words contained in parentheses were removed, as they are added by Hansard to provide additional information not actually spoken by the MP.1 Speeches and data on MPs’ gender and party affiliation are from a previously assembled dataset (Odell, 2018). Information on candidates selected through all women short lists is from the House of Commons Library (Kelly, 2016). Unsuccessful General Election candidates selected through all women short lists who were subsequently elected in a byelection are classified as having been selected on an all women short list.

2.1 Linguistic Inquiry and Word Coun

Word classification used the Linguistic Inquiry and Word Count 2015 (LIWC) dictionary (Pennebaker et al., 2015) and tokenising tools from the Quanteda R package (Benoit, 2018). Word counts and words-per-sentence were calculated using stringi (Gagolewski, 2018), a wrapper to the ICU regex library.

Following Yu (2014) drawing on Newman, Groom, Handelman, & Pennebaker (2008) we used the following LIWC categories:

  • All Pronouns (pronoun)
  • First person singular pronouns (i)
  • First person plural pronouns (we)
  • Verbs (verb)
  • Auxiliary verbs (auxverb)
  • Social processes (social)
  • Positive emotions (posemo)
  • Negative emotions (negemo)
  • Tentative words (tentat)
  • Articles (article)
  • Prepositions (preps)
  • Anger words (anger)
  • Swear words (swear)
  • Cognitive processes (cogproc)
  • Words longer than six letters (Sixltr)

We also included mean words-per-sentence (WPS), total speach word count (WC) and Flesch–Kincaid grade level (FK) (Kincaid, Fishburne, Rogers, & Chissom, 1975), calculated using Quanteda (Benoit, 2018) and stringi (Gagolewski, 2018).

2.2 Women vs Men

Effect Sizes for Male and Female Labour MPs
Women
Men
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.07 4.60 10.15 4.99 0.02 negligible
First person singular pronouns 1.89 2.41 2.02 2.55 0.05 negligible
First person plural pronouns 0.97 1.42 0.99 1.51 0.01 negligible
Verbs 12.82 5.00 12.67 5.36 -0.03 negligible
Auxiliary verbs 7.91 3.45 7.93 3.69 0.01 negligible
Social processes 8.47 4.82 8.18 5.11 -0.06 negligible
Positive emotions 2.73 2.49 2.57 2.54 -0.06 negligible
Negative emotions 1.15 1.68 1.07 1.77 -0.05 negligible
Tentative words 1.48 1.74 1.58 1.90 0.05 negligible
More than six letters 10.58 3.68 10.22 3.92 -0.10 negligible
Articles 7.65 3.30 7.96 3.55 0.09 negligible
Prepositions 12.58 4.42 12.14 4.73 -0.10 negligible
Anger words 0.23 0.81 0.24 0.77 0.01 negligible
Swear words 0.00 0.06 0.00 0.09 0.01 negligible
Cognitive processes 8.68 4.83 8.82 5.15 0.03 negligible
Words per Sentence 43.63 19.68 41.15 20.04 -0.13 negligible
Total Word Count 402.79 691.27 370.18 647.36 -0.05 negligible
Flesh-Kincaid Grade Level 10.81 7.68 9.78 7.87 -0.13 negligible

There are no categories where gender differences meet the effect size threshold of \(|0.2|\) suggested by Cohen (1988, pp. 25–26) to indicate a small effect. 4 categories – words with more than six letters, prepositions, words-per-sentence and Flesh-Kincaid grade level – exceeded the \(|0.1|\) threshold suggested by Newman et al (2008).

2.3 Short lists vs Non-Short lists

The following plots show changes in the occurences of selected LIWC terms, words-per-sentence, total word count and Flesch–Kincaid grade level, over the course of an MP’s career. There do not appear to be any notable changes in speaking style over the course of female Labour MPs’ careers.

\label{sl-key-variables}Occurence of selected LIWC terms

Occurence of selected LIWC terms

Effect Sizes for Female Labour MPs by selection process
All Women Short lists
Open Shorlists
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.01 4.67 10.19 4.48 -0.04 negligible
First person singular pronouns 1.86 2.41 1.95 2.42 -0.04 negligible
First person plural pronouns 0.88 1.36 1.16 1.51 -0.19 negligible
Verbs 12.88 5.10 12.69 4.80 0.04 negligible
Auxiliary verbs 7.94 3.49 7.86 3.38 0.02 negligible
Social processes 8.48 4.94 8.46 4.59 0.00 negligible
Positive emotions 2.69 2.52 2.81 2.42 -0.05 negligible
Negative emotions 1.16 1.69 1.13 1.67 0.02 negligible
Tentative words 1.48 1.75 1.49 1.73 0.00 negligible
More than six letters 10.52 3.73 10.70 3.58 -0.05 negligible
Articles 7.69 3.38 7.55 3.15 0.04 negligible
Prepositions 12.55 4.54 12.63 4.15 -0.02 negligible
Anger words 0.23 0.78 0.24 0.88 -0.01 negligible
Swear words 0.00 0.06 0.00 0.05 0.01 negligible
Cognitive processes 8.59 4.90 8.86 4.69 -0.06 negligible
Words per Sentence 44.02 20.45 42.85 18.05 0.06 negligible
Total Word Count 401.77 704.40 404.78 664.97 0.00 negligible
Flesh-Kincaid Grade Level 10.97 7.97 10.48 7.06 0.07 negligible

There are no categories among female Labour MPs by selection process meeting the \(|0.2|\) threshold. Only one category – first person plural pronouns, d=0.19 – exceeded \(|0.1|\).

2.4 Conservatives vs Labour

Effect Sizes for All Labour and Conservative MPs
Labour
Conservatives
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.12 4.87 10.62 4.84 0.10 negligible
First person singular pronouns 1.98 2.51 2.15 2.56 0.07 negligible
First person plural pronouns 0.98 1.48 1.22 1.70 0.15 negligible
Verbs 12.72 5.24 12.93 5.13 0.04 negligible
Auxiliary verbs 7.92 3.61 8.17 3.58 0.07 negligible
Social processes 8.28 5.02 8.13 4.80 -0.03 negligible
Positive emotions 2.62 2.53 2.85 2.66 0.09 negligible
Negative emotions 1.10 1.74 1.05 1.78 -0.03 negligible
Tentative words 1.55 1.85 1.57 1.88 0.01 negligible
More than six letters 10.34 3.85 10.28 3.76 -0.02 negligible
Articles 7.86 3.48 7.82 3.45 -0.01 negligible
Prepositions 12.28 4.64 12.38 4.49 0.02 negligible
Anger words 0.24 0.78 0.24 0.82 0.01 negligible
Swear words 0.00 0.08 0.00 0.10 0.00 negligible
Cognitive processes 8.77 5.05 8.86 5.06 0.02 negligible
Words per Sentence 41.95 19.96 42.76 20.16 0.04 negligible
Total Word Count 380.71 662.03 335.54 592.41 -0.07 negligible
Flesh-Kincaid Grade Level 10.12 7.82 10.41 7.91 0.04 negligible

There are no categories with effect sizes exceeding \(|0.2|\) between Labour and Conservative MPs, like inter-Labour differences.

2.5 All MPs Gender Differences

There are no categories with effect sizes exceeding \(|0.2|\) when comparing all male and female MPs elected from 1997 onwards. There is only one category, “Articles”, with an effect size of 0.11, greater than the \(|0.1|\) threshold suggested by Newman et al. (2008).

Effect Sizes for Male and Female MPs, All Parties
Women
Men
Effect Size
Mean SD Mean SD Cohen’s D Magnitude
All Pronouns 10.31 4.65 10.26 4.90 -0.01 negligible
First person singular pronouns 1.99 2.45 2.00 2.52 0.00 negligible
First person plural pronouns 1.11 1.57 1.08 1.59 -0.02 negligible
Verbs 12.88 4.97 12.80 5.26 -0.02 negligible
Auxiliary verbs 8.00 3.45 8.08 3.64 0.02 negligible
Social processes 8.45 4.77 8.00 4.93 -0.09 negligible
Positive emotions 2.84 2.53 2.69 2.58 -0.06 negligible
Negative emotions 1.10 1.65 1.08 1.78 -0.01 negligible
Tentative words 1.47 1.73 1.61 1.91 0.08 negligible
More than six letters 19.73 6.94 19.25 7.18 -0.07 negligible
Articles 7.62 3.31 8.00 3.51 0.11 negligible
Prepositions 12.58 4.36 12.22 4.62 -0.08 negligible
Anger words 0.23 0.78 0.25 0.82 0.02 negligible
Swear words 0.00 0.05 0.00 0.10 0.01 negligible
Cognitive processes 8.67 4.79 8.93 5.12 0.05 negligible
Words per Sentence 43.25 19.45 42.06 20.12 -0.06 negligible
Total Word Count 377.31 648.92 358.13 623.49 -0.03 negligible
Flesh-Kincaid Grade Level 10.63 7.61 10.16 7.89 -0.06 negligible

2.6 POS Analysis

Part-of-Speech Effect Sizes for Male and Female Labour MPs
Women
Men
Effect Size
Word Type Mean SD Mean SD Cohen’s D Magnitude
All Nouns 22.18 9.60 21.66 10.96 -0.05 negligible
Plural Nouns 5.85 3.72 5.03 3.79 -0.22 small
Singular Nouns 15.62 9.84 16.01 11.19 0.04 negligible
Adjectives 9.58 4.78 9.28 5.29 -0.06 negligible
Adverbs 4.91 4.26 5.07 4.91 0.03 negligible
Verbs 20.94 9.52 20.78 10.28 -0.02 negligible
Part-of-Speech Effect Sizes for AWS and non-AWS Labour MPs
All Women Short lists
Open Shorlists
Effect Size
Word Type Mean SD Mean SD Cohen’s D Magnitude
All Nouns 22.16 8.78 22.18 10.00 -0.04 negligible
Plural Nouns 6.03 3.60 5.76 3.77 -0.16 negligible
Singular Nouns 15.51 8.97 15.67 10.26 0.02 negligible
Adjectives 9.83 4.59 9.45 4.86 -0.02 negligible
Adverbs 4.95 3.78 4.89 4.49 0.03 negligible
Verbs 20.88 9.04 20.97 9.76 -0.02 negligible

Part-of-speech (POS) tagging was done using spaCy (Honnibal & Montani, 2017) and the spacyr package (Benoit & Matsuo, 2018). There is one small gender difference (d = \(|0.22|\)) in the use of plural nouns, which make up 5.85% of the words used by female Labour MPs, compared to 5.03% of words spoken by male Labour MPs. As with LIWC, there are no categories where d >= \(|0.2|\) when comparing female Labour MPs by selection process.

2.7 Tokenising / Keyness

The most commonly used words by both men and women would be protocol decorum expressions, so we calculate the keyness of words to identify gender differences in the choices of topics raised by men and women, and by short-list and non-short list women.

2.7.1 Men vs Women

Keyness – a linguistic measure of the frequency of different words in two groups of texts – reveals clear gender differences in the most disproportionately common words used by female and male Labour MPs. Unsurprisingly, despite male MPs saying almost twice as many words (30,596,921 vs 15,897,929) as their female colleagues, female Labour MPs were more than two-and-a-half (2.61) times as likely to say “women”. They were also much more likely to use “women’s” and “woman” in parliamentary debate. Female Labour MPs also appear much more likely to discuss “children”, “people”, “care”, “families”, “home”, “parents”, “work” and social policy areas such as “services”, “disabled [people]” and “housing” than their male colleagues. Male MPs were more likely to refer to military topics (“Iraq”, “nuclear”), and to parliamentary process and protocol – “question”, “political”, “conservative”, “electoral”, “house”, “party”, “argument” “liberal” and “point” are far more common in speeches by male Labour MPs than by female ones. This could suggest that male Labour MPs are more comfortable using the traditional language of House of Commons debate, and are more concerned with the rules, procedures and processes of the parliamentary system than their female colleagues.

\label{gender-keyness}Keyness between Labour MPs, by Gender

Keyness between Labour MPs, by Gender

2.7.2 Short lists vs Non-Short lists

Keyness differences by selection process are not as obviously stereotypical. Nonetheless, the most common words amongst AWS MPs included “carers”, “disabled”, “bedroom” and “sen”2. Also of note is AWS MPs making more references to their “constituency” and its “constituents”, suggesting that AWS MPs may draw on the fact they were elected by their constituents as a source political legitimacy, at least more than non-AWS MPs.

\label{sl-keyness}Keyness between Female Labour MPs, by Selection Process

Keyness between Female Labour MPs, by Selection Process

2.7.3 Labour vs Conservative

The keyness differences between Labour and Conservative MPs are much greater than gender differences within Labour. The very high use of “Lady” by Conservative MPs is reflective of the greater proportion of female MPs in other parties, as it is often used to refer to comments by other members of the house. It may also represent a greater use of traditional hosue decorum by Conservative MPs.

\label{party-keyness}Keyness between Labour and Conservative MPs

Keyness between Labour and Conservative MPs

2.8 Bigrams

We created bigrams of all first person plural and singular pronouns for female Labour MPs. As above, AWS MPs are far more likely to make references to their constituency or their constituents.

\label{bigrams-keyness}Bigram Keyness in Female Labour MPs by Selection Process

Bigram Keyness in Female Labour MPs by Selection Process

2.9 Naive Bayes classification

We trained a Naive Bayes classifier with document-frequency priors and a multinomial distribution to predict the gender of speakers when given speeches by all Labour MPs in our dataset, and the selection process when only given female Labour MPs. The accuracy of both models were roughly equivalent, 70.55% accuracy when predicting gender and 70.84% when predicting short lists. By contrast, the classifier could distinguish between Labour and Conservative speeches with 74.23% accuracy.

2.10 Topic Models

Using topic models to classify text is widely used in social sciences (Grimmer & Stewart, 2013), as, when combined with the large volume of plain text data available, it allows for a rapid and consistent method of analysis . Topic modelling and other statistic methods of textual analysis are not a substitute for reading the texts themselves, but can augment other analysis or – as in this case – analyse and classify larger amounts of text than would be feasible using human coders (Grimmer & Stewart, 2013). Topic models classify a series of documents (in this case individual speeches) into one of a given number of topics, identifying terms that are common in some documents but rare in others. When developing topic models, there is a trade-off between high precision in the classification of each document with broader topics when using smaller numbers of topics, or lower precision in individual speech classification with more finely-grained topics when using larger numbers of topics. Grimmer & Stewart (2013) also highlight the importance of validating unsurpervised topic models when applied to new sets of texts.

The R package stm (Roberts, Stewart, & Tingley, 2018) implements a structured topic model (STM) (Arora et al., 2013; Roberts, Stewart, & Airoldi, 2016). An STM incorporates data about the writer or speaker into the topic classification algorithm. This differs from traditional topic modelling methods using latent variables to identify topics (e.g. with latent Dirichlet allocation Blei, Ng, & Jordan, 2003), and then comparing proportions of each topic to one or more external variables. STM allows us to incorporate the variables we are interested in to the topic model itself, i.e. the proportion of speechs classified as belonging to each topic can vary as a function of the AWS variable.

We incorporated the AWS status of speakers into our topic model, using all speeches by female Labour MPs, with their AWS status as a covariate in classifying topics. We then matched these topics to speechs by male Labour MPs.

We produced two different structured topic model implementations, with different numbers of topics (K).

The first implementation used an algorithm developed by Lee & Mimno (2014), implemented in the stm package (Roberts et al., 2018), to estimate the number of topics across all speeches made by female Labour MPs, using the “spectral” method developed by Arora et al. (2013), implemented by Roberts et al. (2018). The resulting topic model has 69 topics, across 81,607 documents and a dictionary of 115,477 words. However, the topic quality with K = 69 is poor, and several topics have poor semantic coherence (see , and the appendix).

2.10.1 Short lists vs Non-Short lists - K30

As seen in the word lists in the appendix, there is relatively scattershot semantic coherence, although exclusivity is high, when using the 69 topic models suggested by Lee and Mimno’s (2014) algorithm. We therefore re-ran the analysis, using 30 topic models, which resulted in increased semantic coherence, albeit with slightly lower exclusivity, as illustrated in Figure . The lower number of models also makes accurate hand-coding of topics possible.

We created a Fruchterman-Reingold (Fruchterman & Reingold, 1991) diagram to show the connections between different topics. Larger vertices indicate more common topics, and the plot uses a colour scale to indicate the proportion of speeches classed in that topic made by AWS and non-AWS female Labour MPs. The space between vertices indicate the closeness of two topics.

\label{k30-network}Fruchterman-Reingold plot of K30 Network

Fruchterman-Reingold plot of K30 Network

\label{k30-coherence}Coherence of K30 Topic Models

Coherence of K30 Topic Models

The stm package includes the estimateEffect function to create a regression model using individual documents (speeches) as individual observations, with the number of documents fitting each topic as the dependent variable and model covariates (AWS status) as independent variables.

In all but five topics (gender, immigration, international, children and the environment) the model found p values of < 0.01, and in every topic except the aforemention and “people”, p values of < 0.001.

Topic Estimates
Estimate Standard Error t value Pr(>|t|)
Topic 1 – Bills
Intercept 0.0449633 0.0006498 69.1927133 < 0.001 ***
Short List -0.0068652 0.0008157 -8.4158760 < 0.001 ***
Topic 2 – Consultations
Intercept 0.0757456 0.0006088 124.4135491 < 0.001 ***
Short List -0.0214950 0.0007211 -29.8072043 < 0.001 ***
Topic 3 – Gender
Intercept 0.0230188 0.0006054 38.0240848 < 0.001 ***
Short List -0.0007313 0.0006927 -1.0557346 0.29
Topic 4 – Police
Intercept 0.0289330 0.0006350 45.5645109 < 0.001 ***
Short List -0.0074441 0.0007453 -9.9881995 < 0.001 ***
Topic 5 – European Union
Intercept 0.0368405 0.0006729 54.7504749 < 0.001 ***
Short List -0.0050989 0.0007784 -6.5507532 < 0.001 ***
Topic 6 – Transport
Intercept 0.0218012 0.0005744 37.9563742 < 0.001 ***
Short List 0.0054380 0.0007675 7.0850991 < 0.001 ***
Topic 7 – Disability
Intercept 0.0298360 0.0006461 46.1800470 < 0.001 ***
Short List 0.0131765 0.0007849 16.7879674 < 0.001 ***
Topic 8 – Immigration
Intercept 0.0186039 0.0004805 38.7189859 < 0.001 ***
Short List 0.0004577 0.0006259 0.7313654 0.46
Topic 9 – Disease
Intercept 0.0249569 0.0005888 42.3893263 < 0.001 ***
Short List -0.0051127 0.0007222 -7.0788362 < 0.001 ***
Topic 10 – Parties
Intercept 0.0378846 0.0004751 79.7467610 < 0.001 ***
Short List 0.0020066 0.0005987 3.3514073 < 0.001 ***
Topic 11 – Health Care
Intercept 0.0407905 0.0006961 58.5999064 < 0.001 ***
Short List -0.0083156 0.0008737 -9.5180094 < 0.001 ***
Topic 12 – Members
Intercept 0.0389968 0.0005993 65.0713124 < 0.001 ***
Short List 0.0082184 0.0007302 11.2551023 < 0.001 ***
Topic 13 – Investment
Intercept 0.0302313 0.0006154 49.1261500 < 0.001 ***
Short List -0.0028620 0.0007741 -3.6972528 < 0.001 ***
Topic 14 – Crime
Intercept 0.0239303 0.0005292 45.2214465 < 0.001 ***
Short List -0.0032121 0.0006371 -5.0418257 < 0.001 ***
Topic 15 – Justice
Intercept 0.0409423 0.0006878 59.5228004 < 0.001 ***
Short List -0.0167466 0.0008429 -19.8683165 < 0.001 ***
Topic 16 – Energy
Intercept 0.0384171 0.0006743 56.9719478 < 0.001 ***
Short List -0.0046100 0.0008719 -5.2874762 < 0.001 ***
Topic 17 – People
Intercept 0.0788563 0.0005752 137.0944198 < 0.001 ***
Short List 0.0019922 0.0007230 2.7555854 0.006 **
Topic 18 – Education
Intercept 0.0298510 0.0006842 43.6284617 < 0.001 ***
Short List 0.0038047 0.0008443 4.5061502 < 0.001 ***
Topic 19 – Fishing
Intercept 0.0196069 0.0004960 39.5281208 < 0.001 ***
Short List 0.0092594 0.0006585 14.0621858 < 0.001 ***
Topic 20 – Housing
Intercept 0.0204434 0.0005486 37.2667515 < 0.001 ***
Short List 0.0038473 0.0007514 5.1199139 < 0.001 ***
Topic 21 – Tax & Budgets
Intercept 0.0460797 0.0007993 57.6466588 < 0.001 ***
Short List 0.0142945 0.0010081 14.1799697 < 0.001 ***
Topic 22 – Farmers
Intercept 0.0139625 0.0004691 29.7626596 < 0.001 ***
Short List 0.0055240 0.0005797 9.5290003 < 0.001 ***
Topic 23 – International
Intercept 0.0261568 0.0006786 38.5431264 < 0.001 ***
Short List 0.0007715 0.0008471 0.9108196 0.36
Topic 24 – Media
Intercept 0.0124115 0.0003960 31.3427056 < 0.001 ***
Short List 0.0048964 0.0004979 9.8338502 < 0.001 ***
Topic 25 – Local authorities
Intercept 0.0426114 0.0006104 69.8123561 < 0.001 ***
Short List -0.0085925 0.0007381 -11.6415862 < 0.001 ***
Topic 26 – Children
Intercept 0.0250044 0.0005685 43.9835308 < 0.001 ***
Short List -0.0000121 0.0007339 -0.0164345 0.99
Topic 27 – Environment
Intercept 0.0149434 0.0004799 31.1365480 < 0.001 ***
Short List 0.0003248 0.0005945 0.5463792 0.58
Topic 28 – Ministers
Intercept 0.0427008 0.0005833 73.2084634 < 0.001 ***
Short List 0.0145502 0.0007220 20.1539326 < 0.001 ***
Topic 29 – Parliament
Intercept 0.0187969 0.0005246 35.8334273 < 0.001 ***
Short List 0.0063043 0.0006868 9.1788680 < 0.001 ***
Topic 30 – Other
Intercept 0.0526683 0.0003251 161.9821372 < 0.001 ***
Short List -0.0037495 0.0003943 -9.5080507 < 0.001 ***
Count and Distribution of Topics – K30
Topic Number AWS Speeches Percent of AWS Speeches Non-AWS Speeches Percent of non-AWS Speeches Male MP Speeches Percent of Male MP Speeches
Topic 1 1,792 3.34% 1,229 4.4% 8,163 4.82%
Topic 2 2,476 4.61% 2,514 9.01% 11,392 6.73%
Topic 3 1,082 2.01% 632 2.27% 926 0.55%
Topic 4 1,303 2.43% 900 3.23% 3,364 1.99%
Topic 5 1,976 3.68% 1,371 4.91% 9,653 5.7%
Topic 6 1,720 3.2% 623 2.23% 4,562 2.69%
Topic 7 2,721 5.07% 758 2.72% 4,045 2.39%
Topic 8 879 1.64% 381 1.37% 2,192 1.29%
Topic 9 1,008 1.88% 743 2.66% 1,747 1.03%
Topic 10 1,351 2.52% 658 2.36% 6,235 3.68%
Topic 11 2,144 3.99% 1,552 5.56% 4,494 2.65%
Topic 12 2,507 4.67% 883 3.16% 10,395 6.14%
Topic 13 1,231 2.29% 825 2.96% 3,972 2.35%
Topic 14 984 1.83% 646 2.32% 1,570 0.93%
Topic 15 1,180 2.2% 1,410 5.05% 4,935 2.91%
Topic 16 2,175 4.05% 1,302 4.67% 7,547 4.46%
Topic 17 5,309 9.89% 2,357 8.45% 25,255 14.91%
Topic 18 2,362 4.4% 1,003 3.59% 6,230 3.68%
Topic 19 1,183 2.2% 445 1.59% 3,305 1.95%
Topic 20 1,334 2.48% 561 2.01% 2,075 1.23%
Topic 21 4,361 8.12% 1,556 5.58% 11,845 6.99%
Topic 22 977 1.82% 359 1.29% 2,259 1.33%
Topic 23 1,787 3.33% 890 3.19% 6,124 3.62%
Topic 24 813 1.51% 233 0.84% 2,132 1.26%
Topic 25 1,604 2.99% 1,104 3.96% 4,917 2.9%
Topic 26 1,237 2.3% 664 2.38% 1,105 0.65%
Topic 27 668 1.24% 325 1.16% 1,796 1.06%
Topic 28 3,218 5.99% 1,001 3.59% 8,906 5.26%
Topic 29 1,121 2.09% 304 1.09% 4,463 2.64%
Topic 30 1,202 2.24% 673 2.41% 3,746 2.21%
\label{k30-topic-pyramid-plot}K30 Pyramid Chart

K30 Pyramid Chart

\label{k30-topic-bar-plot}K30 Bar Chart

K30 Bar Chart

AWS are – proportionally – more likely than non-AWS MPs to discuss Topics 29 (parliament), 7 (disability) and 24 (media). They are proportionally less likely to mention Topics 15 (justice), 2 (consultations) and 9 (disease). See Figure for more details. Perhaps surprisingly, AWS MPs are slightly less likely to mention gender issues (Topic 3), although the difference is not statistically significant (see the appendix for details).

\label{k30-topic-proportion}K30 Topic Proportions

K30 Topic Proportions

2.10.1.1 Word Occurences

Words in Topic - K30
Topic Number Top Ten Words Top Ten FREX
Topic 1 bill, amendment, clause, new, legislation, amendments, act, committee, provisions, 1 amendment, clause, amendments, clauses, nos, insert, subsection, provisions, bill, tabled
Topic 2 issues, public, information, also, report, review, process, work, need, important consultation, review, guidance, recommendations, information, considering, decisions, arrangements, framework, detailed
Topic 3 women, men, pay, equality, rights, women’s, discrimination, equal, work, woman women, equality, gender, equalities, bishops, discrimination, female, women’s, equal, men
Topic 4 police, crime, officers, behaviour, policing, home, antisocial, community, work, force policing, antisocial, constable, burglary, wardens, crime, constabulary, police, officers, pcsos
Topic 5 european, uk, countries, eu, union, trade, international, united, world, british treaty, enlargement, wto, lisbon, doha, eu, eu’s, mod, multilateral, accession
Topic 6 transport, london, rail, bus, road, services, line, travel, network, train rail, bus, passengers, fares, trains, buses, passenger, heathrow, congestion, hs2
Topic 7 people, work, benefit, pension, benefits, support, disabled, employment, carers, working disabled, jobcentre, incapacity, carers, pension, claimants, esa, dla, pensions, atos
Topic 8 immigration, safety, uk, asylum, enforcement, home, number, illegal, licensing, animals dogs, dog, id, visa, fur, mink, hse, sia, seekers, fireworks
Topic 9 health, research, cancer, treatment, medical, disease, can, smoking, patients, people cancer, diseases, vaccine, flu, embryos, infections, diabetes, palliative, prostate, cervical
Topic 10 government, labour, conservative, party, opposition, policy, government’s, scotland, scottish, members conservative, liberal, democrats, conservatives, scottish, democrat, scotland, tory, interruption, tories
Topic 11 care, health, nhs, services, service, hospital, patients, staff, trust, social dentists, ambulance, dentistry, helier, dentist, nurses, hospital, pct, hospitals, dental
Topic 12 member, members, debate, house, mr, committee, said, time, speaker, north member, speaker, mr, debate, spoke, thoughtful, backbench, debates, madam, select
Topic 13 companies, financial, company, market, scheme, money, debt, consumers, bank, credit payday, annuity, oft, policyholders, penrose, fca, loan, prepayment, loans, annuities
Topic 14 young, people, health, mental, youth, prison, problems, drugs, alcohol, drug prisons, probation, cannabis, reoffending, mental, prison, self-harm, youth, alcohol, sentences
Topic 15 cases, court, legal, law, case, justice, evidence, criminal, courts, home judicial, attorney-general, defendant, extradition, tpims, suspects, court, courts, prosecution, isc
Topic 16 energy, businesses, business, jobs, investment, economy, industry, economic, new, sector carbon, renewable, renewables, solar, low-carbon, energy, feed-in, manufacturing, steel, businesses
Topic 17 people, want, one, get, know, say, us, many, think, need things, think, something, get, want, going, really, say, lot, go
Topic 18 education, schools, school, children, training, skills, parents, teachers, students, young schools, teachers, pupils, curriculum, sen, academies, ofsted, pupil, grammar, attainment
Topic 19 constituency, city, people, many, years, work, centre, one, hull, great fishermen, cod, hull, plymouth, maiden, fishing, fish, humber, fleetwood, tourism
Topic 20 housing, homes, people, private, london, social, home, affordable, need, accommodation rent, tenants, landlords, rented, homelessness, homeless, rents, tenancies, housing, tenancy
Topic 21 tax, year, million, government, budget, cuts, cut, poverty, increase, billion tax, obr, vat, millionaires, 50p, inflation, budget, fiscal, chancellor, cut
Topic 22 food, post, office, rural, petition, offices, farmers, royal, mail, government petition, farmers, petitioners, meat, cull, labelling, cattle, badger, culling, beef
Topic 23 people, international, human, government, war, rights, country, un, conflict, world syria, israel, civilians, palestinian, israeli, gaza, sri, holocaust, hatred, sierra
Topic 24 bbc, media, online, internet, sport, access, digital, culture, clubs, football bbc, games, olympic, gambling, bbc’s, copyright, lap-dancing, broadband, radio, internet
Topic 25 local, authorities, funding, areas, services, council, community, authority, government, communities local, authorities, funding, councils, grant, authority, formula, deprived, areas, partnership
Topic 26 children, child, families, care, family, parents, violence, support, domestic, victims trafficked, csa, same-sex, adopters, child, rape, marriages, marriage, sexual, couples
Topic 27 planning, water, development, land, environment, site, sites, flood, environmental, area forestry, biodiversity, masts, habitats, gypsy, flood, waterways, flooding, marine, mmo
Topic 28 secretary, state, house, last, statement, report, said, now, question, answer secretary, statement, state, confirm, official, answer, vol, state’s, letter, written
Topic 29 parliament, wales, vote, commission, political, assembly, people, welsh, elected, charities electoral, polling, gibraltar, voting, assembly, vote, votes, voter, ballot, elections
Topic 30 can, make, ensure, agree, important, take, made, point, sure, welcome agree, aware, sure, ensure, taking, lady, welcome, steps, point, make

2.11 Discussion

There do not appear to be substantial or meaningful differences in the speaking styles of female Labour MPs selected through all women short lists when compared to their female colleagues selected through open short lists using LIWC. This is possibly due to the speaking style dominant in British parliamentary debate, which is more formal than the speech used in most day-to-day conversation. LIWC has American developers, and the dictionary may not be able to capture stylistic differences between American and British English, and may not include words commonly used in formal British English speech, limiting its usefulness in a British context.

There is more gender distinction in some selected terms and topics. AWS MPs are far more likely to make reference to their constituency and their constituents. In the debate between whether MPs should be “delegates” or “trustees” – the “mandate-independence controversy” outlined by Pitkin (1967) – the references to their constituents and constituencies suggests AWS MPs shy away from the Burkean concept of trusteeship and see themselves more as strict representatives of their constituents. In Andeweg & Thomassen’s (2005) typology of ex ante/ex post and above/below political representation, AWS MPs lean towards representation “from below”, although their selection process is ex ante/ex post.

AWS MPs refer to their constituents both specifically and in the abstract, particularly when criticising government policy. For example, in debate on 4th March 2015, Gemma Doyle, than the Labour MP for West Dunbartonshire (elected on an AWS in 2010), when asked if she would give way to Conservative MP Stephen Mosley, responded:

No, I will not [give way], because my constituents want me to make these points, not to give more time to Conservative Members.

On 2nd June 2010, during debate on Israel-Palestine, Valerie Vaz, MP for Walsall South:

My constituents want more than pressure. Will the Foreign Secretary come back to the House and report on a timetable for the discussions on a diplomatic solution, just as we did on Ireland?

On 4th April 2001, Betty Williams, member for Conwy from 1997–2010, raised the case of a wilderness guide in her constituency unable to access parts of the countryside due to foot and mouth disease:

Is my right hon. Friend aware that there is continuing concern about the limited access to the countryside and crags of north Wales? May I draw his attention to the circumstances of my constituent, Ric Potter? Like many others, he has had to travel to Scotland, where there is greater access. Will my right hon. Friend help us to enable people such as Ric Potter to find work in outdoor pursuits?

3 Appendix

3.1 Full topic model summary - K30

## A topic model with 30 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
##       Highest Prob: bill, amendment, clause, new, legislation, amendments, act 
##       FREX: amendment, clause, amendments, clauses, nos, insert, subsection 
##       Lift: #185, #85, 0.003, 05, 1-competences, 1-impact, 1,924 
##       Score: clause, amendment, amendments, bill, provisions, lords, nos 
## Topic 2 Top Words:
##       Highest Prob: issues, public, information, also, report, review, process 
##       FREX: consultation, review, guidance, recommendations, information, considering, decisions 
##       Lift: 1-who, 1,842, 109648, 1402, 151387, 1981-was, 1a-has 
##       Score: consultation, guidance, information, review, committee, issues, process 
## Topic 3 Top Words:
##       Highest Prob: women, men, pay, equality, rights, women's, discrimination 
##       FREX: women, equality, gender, equalities, bishops, discrimination, female 
##       Lift: gender, #112, #neverthelesshepersisted, 1-breast-feed, 1,087, 1,574, 1.57 
##       Score: women, women's, equality, men, gender, discrimination, girls 
## Topic 4 Top Words:
##       Highest Prob: police, crime, officers, behaviour, policing, home, antisocial 
##       FREX: policing, antisocial, constable, burglary, wardens, crime, constabulary 
##       Lift: 1,113, 1.24, 17,614, acpo's, adz, alcohol-free, alleygator 
##       Score: police, crime, officers, policing, antisocial, behaviour, constable 
## Topic 5 Top Words:
##       Highest Prob: european, uk, countries, eu, union, trade, international 
##       FREX: treaty, enlargement, wto, lisbon, doha, eu, eu's 
##       Lift: #420, 0.26, 0.56, 07, 09, 1-2, 1-of 
##       Score: eu, european, countries, treaty, armed, defence, forces 
## Topic 6 Top Words:
##       Highest Prob: transport, london, rail, bus, road, services, line 
##       FREX: rail, bus, passengers, fares, trains, buses, passenger 
##       Lift: #145, 0.1p, 0.45, 0.86, 1-very, 1,122, 1,658 
##       Score: rail, transport, bus, passengers, fares, trains, congestion 
## Topic 7 Top Words:
##       Highest Prob: people, work, benefit, pension, benefits, support, disabled 
##       FREX: disabled, jobcentre, incapacity, carers, pension, claimants, esa 
##       Lift: dla, #400, 0300, 1-to-1, 1,030, 1,052, 1,366 
##       Score: pension, carers, disabled, pensions, allowance, disability, credit 
## Topic 8 Top Words:
##       Highest Prob: immigration, safety, uk, asylum, enforcement, home, number 
##       FREX: dogs, dog, id, visa, fur, mink, hse 
##       Lift: 44a, a8, acoba, arcs, attachment-free, bareboat, bonfires 
##       Score: immigration, asylum, animals, dogs, fireworks, dog, animal 
## Topic 9 Top Words:
##       Highest Prob: health, research, cancer, treatment, medical, disease, can 
##       FREX: cancer, diseases, vaccine, flu, embryos, infections, diabetes 
##       Lift: 1169, 20-fold, ablation, abnormalities, adpkd, aed, anaesthesia 
##       Score: cancer, patients, disease, smoking, health, diagnosis, screening 
## Topic 10 Top Words:
##       Highest Prob: government, labour, conservative, party, opposition, policy, government's 
##       FREX: conservative, liberal, democrats, conservatives, scottish, democrat, scotland 
##       Lift: #nationalistsconfused, 1-but, 1.135, 10,182, 10.91, 1125, 116385 
##       Score: conservative, scottish, party, labour, government, scotland, liberal 
## Topic 11 Top Words:
##       Highest Prob: care, health, nhs, services, service, hospital, patients 
##       FREX: dentists, ambulance, dentistry, helier, dentist, nurses, hospital 
##       Lift: 2.24, 2005-6, 22,600, 422, 5.45pm, 8.03, 8.41 
##       Score: nhs, patients, care, hospital, health, patient, hospitals 
## Topic 12 Top Words:
##       Highest Prob: member, members, debate, house, mr, committee, said 
##       FREX: member, speaker, mr, debate, spoke, thoughtful, backbench 
##       Lift: e-petitions, @daisydumble, @percyblakeney63, 10,000-signature, 1028, 1080, 11.00 
##       Score: member, mr, committee, members, speaker, debate, house 
## Topic 13 Top Words:
##       Highest Prob: companies, financial, company, market, scheme, money, debt 
##       FREX: payday, annuity, oft, policyholders, penrose, fca, loan 
##       Lift: fca, oft, prepayment, #1.8, #20,000, 0.21, 0.84 
##       Score: companies, consumers, fsa, banks, company, customers, consumer 
## Topic 14 Top Words:
##       Highest Prob: young, people, health, mental, youth, prison, problems 
##       FREX: prisons, probation, cannabis, reoffending, mental, prison, self-harm 
##       Lift: cannabis, hawton, poppers, camhs, inmates, reoffending, #230 
##       Score: young, mental, prison, drugs, alcohol, youth, drug 
## Topic 15 Top Words:
##       Highest Prob: cases, court, legal, law, case, justice, evidence 
##       FREX: judicial, attorney-general, defendant, extradition, tpims, suspects, court 
##       Lift: 110-day, abscond, absconded, acquittals, adduce, anti-viral, babar 
##       Score: court, offence, courts, criminal, justice, prosecution, offences 
## Topic 16 Top Words:
##       Highest Prob: energy, businesses, business, jobs, investment, economy, industry 
##       FREX: carbon, renewable, renewables, solar, low-carbon, energy, feed-in 
##       Lift: fossil, sellafield, viyella, energy-intensive, low-carbon, #12.5, #140,000 
##       Score: energy, businesses, jobs, economy, manufacturing, industry, investment 
## Topic 17 Top Words:
##       Highest Prob: people, want, one, get, know, say, us 
##       FREX: things, think, something, get, want, going, really 
##       Lift: 1,027, 2.85, 30s-will, 6.37, 778, about-part, accept-there 
##       Score: people, get, think, things, going, want, say 
## Topic 18 Top Words:
##       Highest Prob: education, schools, school, children, training, skills, parents 
##       FREX: schools, teachers, pupils, curriculum, sen, academies, ofsted 
##       Lift: ema, #8,000, 1,000-pupil, 1,051, 1,100-i, 1,170, 1,204 
##       Score: schools, school, education, children, teachers, pupils, students 
## Topic 19 Top Words:
##       Highest Prob: constituency, city, people, many, years, work, centre 
##       FREX: fishermen, cod, hull, plymouth, maiden, fishing, fish 
##       Lift: #14.4, #66.6, 0.27, 0.51, 1,084, 1,126, 1.41 
##       Score: plymouth, constituency, hull, city, fishing, fish, arts 
## Topic 20 Top Words:
##       Highest Prob: housing, homes, people, private, london, social, home 
##       FREX: rent, tenants, landlords, rented, homelessness, homeless, rents 
##       Lift: right-to-buy, #19, #21.5, #28.5, 1,000-odd, 1,026, 1,083 
##       Score: housing, homes, rented, rent, tenants, landlords, affordable 
## Topic 21 Top Words:
##       Highest Prob: tax, year, million, government, budget, cuts, cut 
##       FREX: tax, obr, vat, millionaires, 50p, inflation, budget 
##       Lift: 0.38, 1,869, 107,500, 11.2, 13,600, 2,073, 2.33 
##       Score: tax, cuts, budget, poverty, chancellor, unemployment, billion 
## Topic 22 Top Words:
##       Highest Prob: food, post, office, rural, petition, offices, farmers 
##       FREX: petition, farmers, petitioners, meat, cull, labelling, cattle 
##       Lift: #450, 1072, 11,900, 12-point, 934, a690, ablewell 
##       Score: food, farmers, petitioners, petition, post, rural, offices 
## Topic 23 Top Words:
##       Highest Prob: people, international, human, government, war, rights, country 
##       FREX: syria, israel, civilians, palestinian, israeli, gaza, sri 
##       Lift: muslims, #aleppo, #no2lgbthate, 0.002, 1,000-almost, 1,010, 1,019 
##       Score: syria, un, israel, humanitarian, iraq, palestinian, israeli 
## Topic 24 Top Words:
##       Highest Prob: bbc, media, online, internet, sport, access, digital 
##       FREX: bbc, games, olympic, gambling, bbc's, copyright, lap-dancing 
##       Lift: age-restricted, age-verification, aquatics, bacta, bandwidth, bbfc, bduk 
##       Score: bbc, sport, tickets, internet, digital, online, football 
## Topic 25 Top Words:
##       Highest Prob: local, authorities, funding, areas, services, council, community 
##       FREX: local, authorities, funding, councils, grant, authority, formula 
##       Lift: 416,000, 596,000, 82-3, 885, allison's, baccy, bellwin 
##       Score: local, authorities, funding, councils, authority, council, services 
## Topic 26 Top Words:
##       Highest Prob: children, child, families, care, family, parents, violence 
##       FREX: trafficked, csa, same-sex, adopters, child, rape, marriages 
##       Lift: @mandatenow, 1-regardless, 1,000-discriminates, 1,142,600, 1,483, 1,746, 10-month-old 
##       Score: child, children, parents, violence, care, sexual, rape 
## Topic 27 Top Words:
##       Highest Prob: planning, water, development, land, environment, site, sites 
##       FREX: forestry, biodiversity, masts, habitats, gypsy, flood, waterways 
##       Lift: biodiversity, encampments, masts, #tartantories, 0fficial, 1,000-year-old, 1,251 
##       Score: planning, land, flood, marine, sites, water, site 
## Topic 28 Top Words:
##       Highest Prob: secretary, state, house, last, statement, report, said 
##       FREX: secretary, statement, state, confirm, official, answer, vol 
##       Lift: 12.40, ashleys, ayia, burne, cabinet's, cairns's, clutha 
##       Score: secretary, state, statement, answer, confirm, inquiry, leader 
## Topic 29 Top Words:
##       Highest Prob: parliament, wales, vote, commission, political, assembly, people 
##       FREX: electoral, polling, gibraltar, voting, assembly, vote, votes 
##       Lift: @leamingtonsbc, @maggieannehayes, @nhconsortium, #keeptweeting, 1-46, 1-would, 1,294 
##       Score: electoral, vote, elections, wales, assembly, referendum, welsh 
## Topic 30 Top Words:
##       Highest Prob: can, make, ensure, agree, important, take, made 
##       FREX: agree, aware, sure, ensure, taking, lady, welcome 
##       Lift: 1565, 19602, 2,095, 42931, 94254, agencies-an, anguish-filled 
##       Score: agree, aware, thank, ensure, point, lady, can

3.2 Full topic model estimate summary - K30

## 
## Call:
## estimateEffect(formula = 1:30 ~ short_list, stmobj = topic_model_k30, 
##     metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0449755  0.0006472  69.493 <0.0000000000000002 ***
## short_listTRUE -0.0068779  0.0008151  -8.439 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 2:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0757511  0.0006095  124.28 <0.0000000000000002 ***
## short_listTRUE -0.0215037  0.0007218  -29.79 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 3:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0230304  0.0006035  38.161 <0.0000000000000002 ***
## short_listTRUE -0.0007445  0.0006951  -1.071               0.284    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 4:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0289390  0.0006449  44.870 <0.0000000000000002 ***
## short_listTRUE -0.0074465  0.0007507  -9.919 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 5:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0368433  0.0006683  55.128 < 0.0000000000000002 ***
## short_listTRUE -0.0050970  0.0007776  -6.555      0.0000000000559 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 6:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0217966  0.0005682   38.36 < 0.0000000000000002 ***
## short_listTRUE 0.0054412  0.0007568    7.19    0.000000000000653 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 7:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0298430  0.0006534   45.68 <0.0000000000000002 ***
## short_listTRUE 0.0131664  0.0007960   16.54 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 8:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0186040  0.0004866  38.233 <0.0000000000000002 ***
## short_listTRUE 0.0004542  0.0006331   0.717               0.473    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 9:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0249398  0.0005900  42.274 < 0.0000000000000002 ***
## short_listTRUE -0.0050920  0.0007184  -7.087     0.00000000000138 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 10:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0378826  0.0004769  79.442 < 0.0000000000000002 ***
## short_listTRUE 0.0020076  0.0005988   3.353             0.000801 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 11:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0407964  0.0006946  58.736 <0.0000000000000002 ***
## short_listTRUE -0.0083296  0.0008782  -9.485 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 12:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0389920  0.0005983   65.17 <0.0000000000000002 ***
## short_listTRUE 0.0082206  0.0007271   11.31 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 13:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0302234  0.0006155  49.102 < 0.0000000000000002 ***
## short_listTRUE -0.0028531  0.0007772  -3.671             0.000241 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 14:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0239267  0.0005272   45.38 < 0.0000000000000002 ***
## short_listTRUE -0.0032073  0.0006326   -5.07          0.000000398 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 15:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0409498  0.0006912   59.24 <0.0000000000000002 ***
## short_listTRUE -0.0167429  0.0008410  -19.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 16:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0384203  0.0006693  57.403 < 0.0000000000000002 ***
## short_listTRUE -0.0046108  0.0008739  -5.276          0.000000132 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 17:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0788541  0.0005734 137.511 < 0.0000000000000002 ***
## short_listTRUE 0.0019922  0.0007215   2.761              0.00576 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 18:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0298378  0.0006936  43.018 < 0.0000000000000002 ***
## short_listTRUE 0.0038174  0.0008514   4.483           0.00000735 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 19:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0196109  0.0005058   38.77 <0.0000000000000002 ***
## short_listTRUE 0.0092466  0.0006647   13.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 20:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0204377  0.0005469  37.372 < 0.0000000000000002 ***
## short_listTRUE 0.0038546  0.0007434   5.185          0.000000216 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 21:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0460818  0.0008017   57.48 <0.0000000000000002 ***
## short_listTRUE 0.0143004  0.0010069   14.20 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 22:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0139579  0.0004672  29.879 <0.0000000000000002 ***
## short_listTRUE 0.0055290  0.0005755   9.608 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 23:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0261672  0.0006795  38.507 <0.0000000000000002 ***
## short_listTRUE 0.0007523  0.0008413   0.894               0.371    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 24:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0124090  0.0003942  31.477 <0.0000000000000002 ***
## short_listTRUE 0.0049032  0.0004912   9.983 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 25:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0426122  0.0006095   69.91 <0.0000000000000002 ***
## short_listTRUE -0.0085951  0.0007406  -11.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 26:
## 
## Coefficients:
##                   Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     0.02500845  0.00056664  44.134 <0.0000000000000002 ***
## short_listTRUE -0.00001695  0.00073195  -0.023               0.982    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 27:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0149391  0.0004787  31.208 <0.0000000000000002 ***
## short_listTRUE 0.0003306  0.0005963   0.554               0.579    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 28:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0427050  0.0005816   73.42 <0.0000000000000002 ***
## short_listTRUE 0.0145487  0.0007200   20.21 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 29:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0187999  0.0005248  35.823 <0.0000000000000002 ***
## short_listTRUE 0.0063006  0.0006838   9.214 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 30:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0526695  0.0003205 164.348 <0.0000000000000002 ***
## short_listTRUE -0.0037490  0.0003892  -9.634 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

3.3 AWS References to Constituents in Context

A random selection of 2% of all references to “my constituency”, “my constituent” and “my constituents”, by AWS MPs, in context.

A random sample of KWIC’s
Pre Keyword Post
. Let me take this opportunity to welcome initiatives in my constituency , particularly the Skypad unit , which is based at
pill . Can my right hon . Friend say where my constituents can turn now for the justice they ought to receive
that there has been an incredible growth , certainly in my constituency , in the number of people having to resort to
During the two years I have been meeting farmers in my constituency and making representations to Ministers , many of the issues
My constituent Enola Halleron-Clarke , who is 11 years old , suffers
The George Thomas hospice in my constituency-a charity named after one of your predecessors , Mr .
broad thrust of the Budget is very bad news for my constituents . Hull North will see more individuals out of work
Ratcliffe and Gretton . Yes , as the name of my constituency suggests , the largest town is that of Burton upon
but it is of no interest whatsoever to most of my constituents , who do not have the time to intellectualise about
like some clarification of how it will affect people in my constituent’s position .  n "
has caused widespread and persistent congestion on the roads in my constituency . However , the story of Bradford is not unique
it has at St Helier hospital which serves people in my constituency . It has , however , been full of praise
Churches in my constituency have a link with Lesotho that goes back many years
n " , " I want to set out what my constituents would like to happen . First , I will address
effort has failed . This is such an occasion . My constituent , Mr . Aranda , first saw me at one
. and hon . Members , I have been encouraging my constituents to take advantage of the scheme . Will the Minister
to conclude my remarks .  n " , " My constituents ask me these questions . What happens if Lewisham is
housing ? Will he applaud the initiatives already taken in my constituency , and especially in the Vauxhall area , where health
welcome that £ 200 . What advice can he give my constituents when faced with high council tax hikes from the Liberal
become more competitive . I have many small businesses in my constituency . Since I was elected , they have repeatedly raised
investment in innovators in microgeneration , such as those in my constituency , so that people can make genuine choices about fuel
Jobcentre Plus . The Scottish National party Government have excluded my constituency from any assistance such as from enterprise zones and the
Have the Government any plans to tackle this and help my constituents get on the housing ladder ?
constituency . I have urban areas to the north-east of my constituency and a very large rural area running right up to
Allison’s chemist in Cockermouth , which is in my constituency , provided a very important resource for local people after
supply ? Residents in Sway road , Morriston , in my constituency have been threatened with such action because of a fault
been contacted by nearly 50 local police officers living in my constituency . Not only are they fearful for their jobs but
described as the end of local democracy . Many of my constituents might argue that that has already happened , given that
councils , so will the Secretary of State explain to my constituents why county councils are getting additional moneys , but not
, 29 April , and 23 May , but for my constituents this may not happen until the process is further down
constituency are viable businesses that benefit the community , and my constituents want them to remain so . Will he confirm that
I am really concerned about one of the schools in my constituency-I have mentioned it to Ministers before-where only 11 per cent
and secured against their own homes were being quoted to my constituents . Was this a condition the council set for the
. Notwithstanding the fact that the track that would benefit my constituents lies in her constituency , her constituents would benefit from
, the 37 AONBs , including the Wye valley in my constituency , and the Council for the Protection of Rural England
in my constituency . It is the biggest employer in my constituency , so it is extremely important to me and my
grateful for this opportunity to pass on the thanks of my constituents to the Government for being serious about tackling health inequalities
success since I made my maiden speech on employment in my constituency during the debate on the first Queen’s Speech under the
a credit to Westminster .  n " , " My constituency gave the world household names such as Pilkington and Beechams
much power just in transmission . As a gentleman in my constituency says , it is always either windy , sunny or
paying . They have not met the 4,100 people in my constituency who cannot find work-more than a quarter of them are
a new building for the Methodist community in Boothstown in my constituency . It will be a genuinely multi-use building . In
Cumbria and near-misses in many other places , including in my constituency . We know that the emergency services are providing excellent
coverage of rugby league , which is hugely popular in my constituency .
this change in this Bill to ensure that electors in my constituency never have to have this terrible experience again .
banks , such as the Brick in the centre of my constituency , are lengthening by the day . In the past
, what guarantees can the Secretary of State give to my constituents that they will be fully informed of the risks associated
energy companies and changes to the energy company obligation , my constituent may no longer get his hard-to-heat , solid-wall home insulated
 n " , " No sense of understanding of my constituent’s position was given in that final paragraph . The CSA
, it looked at Barton Hill , an estate in my constituency that is in a ward ranked 133rd on the national
Trafford , which has a Conservative council and is where my constituency is located . We are seeing a twin squeeze ,
world . It is a particular issue for many of my constituents , and the violence and human rights abuses have spanned
bandied around-because I want to talk about the reality that my constituency faces . Our health and social provision has developed into
of life and death .  n " , " My constituent Royston Brett set off on Friday and has cycled almost
to some people than to others . For people in my constituency , the Government’s changes so far have resulted in a
Bill on social care , if the Minister thinks that my constituents are happy with the package we have before us at
Alstom Power , a company that makes gas turbines in my constituency , has taken up the challenge . At its conference
Chamber , I have been contacted by a number of my constituents who are concerned about the Bill and would rather see
again have a university . However , Nene college in my constituency hopes to change all that , and I support strongly
That is the right way forward . Each person in my constituency gets £ 7 less than in the neighbouring constituency of
, but those have not materialised , leaving women in my constituency and many others across the UK facing hardship , stress
in this debate , which is of great importance to my constituency . I wish to draw in particular from the circumstances
I have been making complaints on behalf of my constituents for some time about the poor performance of rail services
interest that I wish to raise . A number of my constituents are families bereaved as a result of the Hillsborough disaster
, which have already proved important in creating jobs in my constituency . Is he aware that many employers , including those
I have described , and frustration on the part of my constituent , we received the letter dated 18 September that I
women . If the services were moved , some of my constituents simply could not access them as they should .  
health problems ? Will he look into the case of my constituent , Mrs . Jenkins , whose disability living allowance was
hope I will soon be in a position to reassure my constituents that much-needed investment in Lewisham homes will be forthcoming .
to say that we have some of that excellence in my constituency , with companies such as Baxi Potterton , Aircelle ,
. Mullin ) . This is a big issue in my constituency , where inappropriate development on garden sites is taking place
west coast services that link Bangor and Llandudno , in my constituency , to London . As a regular user , and
and Sir Malcolm Thornton . All have represented part of my constituency and all left this House on 20 April or 1
When I met my constituents in Highway ward on Saturday , they told me that
can make a clear distinction between the two . In my constituency , one thing of which I am most proud is
matter of great importance to the people of Merseyside and my constituents , even though it relates to the South Yorkshire police
prison . An ombudsman’s inquiry takes time and , as my constituent says , time is not on his side . In
they spend their money .  n " , " My constituency has a close relationship with the textiles industry . Recently
The outcome of the consultations , e-consultations and debates around my constituency was finely balanced . A small majority felt that there
institutions , customers and members , but just as all my constituents stand to gain from the contribution that the Bill can
not sure whether that is correct because several hundred of my constituents write to me regularly . For example , Carrie Flint
Over the weekend , 400 job losses were announced in my constituency , with the closure of the Stampworks in Ayr ,
many communities because 12 of the 48 post offices in my constituency are proposed for closure . The Government recognise the social
which was only yesterday . I sent a response from my constituency . I can hope only that it had some influence
39 of the Crown post offices , including Lancaster in my constituency-and the relationship MPs have with Post Office Ltd ? Many
’ Bills were introduced on this issue , one by my constituency neighbour , my hon . Friend the Member for Brigg
expensive failure ? It has been a cost-effective success in my constituency .
already taken several interventions .  n " , " My constituent was in work and owned his own home , and
services , for the time that he took to greet my constituents and for the care and the honesty with which he
remain extremely important employers in Burton upon Trent , in my constituency . There has been a gradual decline in the number
legacy of BSE and , in the northern area of my constituency , cattle testing positive for TB , which is a
of the problems , and one issue that some of my constituents have raised is that written reports from their doctors or
by the necessary funding arrangements , so that families in my constituency will see the materialisation of extra child care places that
In my constituency we obviously welcome the two new aircraft carriers and the
everything will be fine .  n " , " My constituent Joe-one of the many people I talked to in the
pooh-poohing of the Bevan Foundation’s inquiry and report , but my constituency took part in that inquiry and I did not see
how important the review of children’s heart surgery is for my constituents , as it is for those of each of the
she work with other Departments , so that pensioners in my constituency and elsewhere can have good , warm housing , a
certainly not going to provide for as many people in my constituency as EMA did . Although Conservative Members can talk about
is perhaps because I have a truly magnificent cathedral in my constituency that is over 1,000 years old that I feel strongly
, but it is an issue of real importance to my constituency .  n " , " Cockenzie’s coal-fired power station
tax will cost our local economy £ 1.9 million in my constituency and almost twice that in Salford , because tenants will
to deliver solar panels on public and community buildings in my constituency . It told me , in relation to the cut
" Many of my constituents who work in the probation service have written to me
she aware of very similar problems at Samworth Brothers in my constituency ? Long-serving workers are seeing their night shift and weekend
 n " , " Many small business people in my constituency are struggling to stay afloat , particularly in the face
, but I can tell the hon . Gentleman that my constituents do not see it as fair .  n "
he aware that although PEP is available to some of my constituents in Hove and Portslade , it is unfortunately not available
a year . I know that many such people in my constituency will not get any benefit afterwards because they will probably
only language I speak fluently is English . But in my constituency in the city of Bristol , 91 different languages are
local licensing schemes , because in my experience , in my constituency , people want more regulation , not less ?
sympathise with the anxieties of people in Ruislip-Northwood , but my constituents in north Paddington very warmly welcome the decision on the
Adjournment debate , I would like to pay tribute to my constituent Nicola Braniff , the partner of the late Stephen O’Malley
throughout the constituency . I will strive to serve all my constituents to the best of my ability .  n "
agree with the sentiment .  n " , " My constituent continues :  n " , " " I could
to present this petition on UK aid on behalf of my constituents . The signatures were  n " , " gathered
that organisations such as Rucksack and other small charities in my constituency , such as Harbour Place , will say that they
, much to my disappointment and the huge disappointment of my constituency and borough , which had the second-largest vote in the
next to impossible , as there is no way for my constituents to contact Concentrix . The HMRC has a hotline for
for that helpful reply . Does she appreciate that in my constituency , during a period of about a year , two
we need for our industry . We are lucky in my constituency to have rail and the M4 , which means that
I believe that my constituency contains more BNP councillors than any other constituency in the
I was-at Calderhead school in Shotts , which is still my constituency . I clearly recall that , when I asked those
havoc they wreak on estates in my constituency , and my constituency is not an exception . If we are serious about
consumer protection Bill , which will provide important safeguards for my constituents . People in Northampton carry high levels of credit .
In my constituency , long-term unemployment has increased by almost 600 % in
Many of my constituents will have been deeply concerned by the admission of Peter
up and take part in this debate and to answer my constituents ’ questions . I have something of interest to tell
to young girls , many of whom are born in my constituency . It is totally unacceptable that the human rights of
to happen .  n " , " People in my constituency have looked at the range of care that needs to
the specific case-which I can refer to him-of one of my constituents who was bankrupted after a VAT inquiry ?
I want to share with the House one anecdote from my constituency . When I was campaigning and canvassing in the general
time to debate miscarriages of justice ? Last night , my constituent , Mr . Michael O’Brien , accepted £ 300,000 in
, as others have pointed out this morning . In my constituency , there is a significant problem with assaults in the
, Sue Essex , has been a huge success . My constituents have told me that it has enabled many of them
make such a major difference at the north end of my constituency .
Does he agree , however , that the lives of my constituents and many others are blighted by these trees , and
As well as big universities such as the ones in my constituency , I am concerned about smaller universities , which often
that was going into the eco-village in Weardale , in my constituency ? That would have created many green jobs in an
insurance industry . What is she going to say to my constituents ?
the affected areas of need . The Weaver ward in my constituency is part of the Staffordshire-Derbyshire objective 5b area , demonstrating
On 6 December , my constituent , Kabba Kamara , was tragically stabbed to death while
.  n " , " Like many others , my constituency is one of great contrasts , from the leafy streets
. We are on the west coast main line . My constituency contains four rail stations , three of which are on
Mary Stevens hospice in Stourbridge is much loved by all my constituents-so much so that it derives 82 per cent . of
to the most vulnerable consumers . Many of those in my constituency are forced to use expensive prepayment cards ; what is
the Minister for Pensions Reform for meeting me to discuss my constituents ’ case . I hope that amendments to the Bill
n " , " Let me highlight three examples from my constituency caseload that illustrate the vulnerability of many people who have
constituents . It is probably the most important issue in my constituency , and has been for a considerable time . It
clients , people seeking debt advice in East Lothian , my constituency , are now saddled with average payday loan debts of
who was concerned about the level of CCTV coverage in my constituency . That speaks volumes when we take into account the
framework for protection from discrimination has been won , and my constituents have shown consistent support for such measures . I congratulate
standards officers are aware of many similar examples . In my constituency of Luton , South , scare tactics are used to
and guns .  "  n " , " My constituent went on to tell me that he had discovered on
Is my right hon . Friend aware that many of my constituents would like to buy British products in recognition of the
many of the early asbestosis claims from Hebden Bridge in my constituency might not have succeeded under the proposed 75 per cent
At least nine members of the PRS are based in my constituency , and the wider south-west is blessed with prolific and
that is not regulated properly , with the result that my constituents , who have small sums of money available to invest
my constituency will get a welcome and much-needed boost . My constituency includes Whitchurch comprehensive school , the biggest comprehensive school in
In 2010 , I had three jobcentres in my constituency . Old Swan was closed by the Minister’s Department at
My constituent , Richard Belmar , has now spent nearly three years
on our streets .  n " , " In my constituency those officers are declining in number , yet the area
Two of my constituents , Jeanette Macleod and Margaret Prior , have both received
go ahead . There is huge concern about this in my constituency and across the north . Was the Prime Minister told
a commitment would be warmly welcomed by Corus workers in my constituency and elsewhere in the UK .
know they are all feeling the pain . Unemployment in my constituency has jumped by 16.2 % . We now have the
ago , I visited XLP , a charity based in my constituency and operating across London to tackle gangs and violent youth
should have such a centre in Northampton , so that my constituents can get proper access to justice to help them with
the moment .  n " , " People in my constituency tell me that their biggest concerns are about jobs .
capital developments are either planned or already in progress . My constituents are really seeing the benefits of massive investment in the
to consider how the regulator is responding to requests from my constituents , who have to wait until the gas supplier receives
nearly two decades without being able to contact them . My constituent is in litigation against the police , and feels a
at hand . Given the high level of interest in my constituency , I recently held a listening event that was kindly
Wilberforce Freedom fair trade coffee , which is produced in my constituency of Hull , so there are commemorative projects of that
" , " bring to her attention the situation of my constituent George Rolph , who is currently on the 23rd day
London , with nearly 13 people chasing every job in my constituency . As a result of the cuts in the public
. Would he like to visit the children’s centres in my constituency that have no children ?
long-term unemployment has gone up . More and more of my constituents are dependent on food banks that operate in my constituency
care that they deserve , particularly during difficult times . My constituents will welcome the Bill for its clarity and fairness .
" A major supermarket is opening in Cefn Mawr in my constituency next Monday , and I welcome that . I welcome
for schools .  " The outcome was that in my constituency and in many areas like it , 30 % of
for the people of Welwyn Hatfield , but I know my constituents sent me here to win them more jobs , bring
not a problem for me or for the people in my constituency . In fact , they would probably like to see
another young boy has been tragically stabbed to death in my constituency . Myron , a talented young rapper , was well
to invigorate the UK and radically transform east London and my constituency , which I am honoured to serve . As with
that a large number of hard-working , two-income families in my constituency will be particularly badly hit by any move from a
work force .  n " , " Thankfully for my constituents as customers of the postal system , those differences have
Loaves and Fishes food and bank in Easington Lane in my constituency . It opened last September and is one of many
, which works hand in hand with the police in my constituency ?
my primary care trust in north-east Derbyshire and dentists in my constituency to find a local solution . These reforms coincide with
ago . It will come as a huge relief to my constituents , who all express the view not only that this
hon . Members , with the motorcycle industry and with my constituents . My hon . Friend the Member for Rhondda introduced
heartening thing about spending time with PCSOs-as I did in my constituency on Friday-is the number of people who know their names
can be used to improve the job opportunities available to my constituents .  n " , " Burnley is a mere
trade with Europe , and the thousands of people in my constituency who have found work through the support of the European
years ago there was massive under-enumeration of the population in my constituency and my borough of Westminster , as well as in
massive part of the cost of living for many of my constituents .  n " , " Last week in my
and communities . I know that only too well from my constituency of Wigan .  n " , " Although I
on salaries of over £ 150,000 . I think that my constituents will see the tax as a just tax , and
investment , the result of which can be seen throughout my constituency through the renewal of children’s play areas , the relaying
the local force polices , and although vehicle crime in my constituency has increased over the past year , it has reduced
tried to follow up on the Prime Minister’s pledge to my constituents , his officials said that no help was forthcoming .
ready for implementation . It will also delight thousands of my constituents who have raised with me the threat of climate change
has its main offices in Swansea , where many of my constituents work . Why on earth we are here again ,
in the country . Mr Ash Naghani , one of my constituents , told a local newspaper :  n " ,
would do for the 4,000 people who are unemployed in my constituency of Leeds West .  n " , " One
reductions in incomes to ordinary people in benefits . In my constituency the average working-age adult is losing £ 560 per year
inequalities .  n " , " Two wards in my constituency have high numbers of people caring for people with stroke
seriously , and their perpetrators must be punished properly . My constituents and I certainly do not want to see a 50
to rise to my feet again . In Hackney in my constituency , we have a new city academy , to which
improve such measures as are in place to ensure that my constituents benefit as much as possible from the meagre offering they
One of my constituents recently had his adoption allowance cut because his child received
out whether the issues that dominate in those areas of my constituency that have most need , most crime and most deprivation
n " , " One of the ceramics companies in my constituency , Naylor , is a family company , not foreign-owned
, such as those at Mount Pleasant sorting office in my constituency , are protected ?
is certainly one reason why the measure is popular in my constituency and elsewhere . The Tories turned the welfare state into
is under threat of being withdrawn-a very important issue for my constituents .  n " , " The petition states that
hear that life expectancy in the more deprived parts of my constituency is lower than the life expectancy of people living in
particular constituency issue-the big threat to the Llanishen reservoir in my constituency , and the threat to Cardiff as a whole .
be at different schools , miles apart . Many of my constituents do not have cars , so it can be almost
a well attended housing information event in my constituency ? My constituents were engaged in it , and were interested to learn
I shall vote for certainty and a better deal for my constituents .  n "
Dundee West ( Jim McGovern ) , a number of my constituents have been on the employment and support programme for two
aware that , during the summer recess , Honda in my constituency announced another 700 jobs to manufacture the new Civic ?
pleased to have secured this important debate on behalf of my constituents in Cumnock and Girvan . I will shortly present to

3.3.1 Short lists vs Non-Short lists - K69

The first implementation used an algorithm developed by Lee & Mimno (2014), implemented in the stm package (Roberts et al., 2018), to estimate the number of topics across all speeches made by female Labour MPs, using the “spectral” method developed by Arora et al. (2013), implemented by Roberts et al. (2018). The resulting topic model has 69 topics, across 81,607 documents and a dictionary of 115,477 words. However, the topic quality with K = 69 is poor, and several topics have poor semantic coherence (see ).

There are several clusters of topics in . For instance, we can see the closeness of Topic 15 (unemployment) and Topic 43 (housing), as both are social issues include discussions of budgets and costs, while Topics 23 (bill amendments) and 16 (education) are very far apart.

\label{k69-network}Fruchterman-Reingold plot of K69 Network

Fruchterman-Reingold plot of K69 Network

\label{k69-coherence}Coherence of K69 Topic Models

Coherence of K69 Topic Models

Count and Distribution of Topics – K69
Topic Number AWS Speeches Percent of AWS Speeches Non-AWS Speeches Percent of non-AWS Speeches Male MP Speeches Percent of Male MP Speeches
Topic 1 1,272 2.37% 353 1.27% 3,434 2.03%
Topic 2 334 0.62% 127 0.46% 1,091 0.64%
Topic 3 241 0.45% 71 0.25% 427 0.25%
Topic 4 550 1.02% 133 0.48% 835 0.49%
Topic 5 826 1.54% 206 0.74% 2,452 1.45%
Topic 6 978 1.82% 915 3.28% 4,060 2.4%
Topic 7 648 1.21% 236 0.85% 1,770 1.05%
Topic 8 70 0.13% 25 0.09% 125 0.07%
Topic 9 265 0.49% 309 1.11% 862 0.51%
Topic 10 1,024 1.91% 513 1.84% 1,065 0.63%
Topic 11 940 1.75% 580 2.08% 3,793 2.24%
Topic 12 313 0.58% 319 1.14% 1,309 0.77%
Topic 13 325 0.61% 146 0.52% 1,181 0.7%
Topic 14 1,596 2.97% 461 1.65% 2,885 1.7%
Topic 15 1,386 2.58% 642 2.3% 4,686 2.77%
Topic 16 1,407 2.62% 525 1.88% 3,651 2.16%
Topic 17 3,690 6.87% 1,459 5.23% 19,359 11.43%
Topic 18 1,026 1.91% 847 3.04% 4,760 2.81%
Topic 19 640 1.19% 423 1.52% 2,130 1.26%
Topic 20 872 1.62% 216 0.77% 2,262 1.34%
Topic 21 658 1.23% 363 1.3% 914 0.54%
Topic 22 818 1.52% 439 1.57% 1,965 1.16%
Topic 23 795 1.48% 518 1.86% 3,553 2.1%
Topic 24 385 0.72% 199 0.71% 1,079 0.64%
Topic 25 240 0.45% 74 0.27% 422 0.25%
Topic 26 788 1.47% 200 0.72% 1,738 1.03%
Topic 27 266 0.5% 120 0.43% 1,010 0.6%
Topic 28 847 1.58% 350 1.25% 3,135 1.85%
Topic 29 1,110 2.07% 327 1.17% 944 0.56%
Topic 30 1,132 2.11% 462 1.66% 6,444 3.81%
Topic 31 996 1.85% 975 3.49% 6,077 3.59%
Topic 32 76 0.14% 64 0.23% 335 0.2%
Topic 33 1,238 2.31% 985 3.53% 6,613 3.9%
Topic 34 1,124 2.09% 521 1.87% 3,335 1.97%
Topic 35 650 1.21% 657 2.35% 2,294 1.35%
Topic 36 601 1.12% 154 0.55% 548 0.32%
Topic 37 455 0.85% 194 0.7% 1,554 0.92%
Topic 38 1,246 2.32% 991 3.55% 2,849 1.68%
Topic 39 1,917 3.57% 936 3.35% 7,664 4.53%
Topic 40 848 1.58% 290 1.04% 2,419 1.43%
Topic 41 63 0.12% 40 0.14% 204 0.12%
Topic 42 853 1.59% 590 2.11% 2,016 1.19%
Topic 43 1,344 2.5% 604 2.16% 2,266 1.34%
Topic 44 814 1.52% 288 1.03% 3,005 1.77%
Topic 45 602 1.12% 474 1.7% 1,086 0.64%
Topic 46 709 1.32% 150 0.54% 1,646 0.97%
Topic 47 664 1.24% 245 0.88% 2,992 1.77%
Topic 48 940 1.75% 901 3.23% 3,045 1.8%
Topic 49 835 1.55% 563 2.02% 2,537 1.5%
Topic 50 1,328 2.47% 1,219 4.37% 3,421 2.02%
Topic 51 1,076 2% 323 1.16% 2,453 1.45%
Topic 52 196 0.36% 85 0.3% 758 0.45%
Topic 53 590 1.1% 293 1.05% 746 0.44%
Topic 54 1,057 1.97% 824 2.95% 5,570 3.29%
Topic 55 302 0.56% 157 0.56% 868 0.51%
Topic 56 535 1% 398 1.43% 847 0.5%
Topic 57 656 1.22% 314 1.13% 1,990 1.18%
Topic 58 468 0.87% 182 0.65% 1,125 0.66%
Topic 59 426 0.79% 183 0.66% 700 0.41%
Topic 60 562 1.05% 297 1.06% 1,389 0.82%
Topic 61 86 0.16% 28 0.1% 174 0.1%
Topic 62 550 1.02% 343 1.23% 746 0.44%
Topic 63 690 1.28% 252 0.9% 1,726 1.02%
Topic 64 594 1.11% 244 0.87% 2,247 1.33%
Topic 65 662 1.23% 457 1.64% 907 0.54%
Topic 66 1,493 2.78% 527 1.89% 4,073 2.41%
Topic 67 737 1.37% 451 1.62% 3,237 1.91%
Topic 68 279 0.52% 145 0.52% 547 0.32%
Topic 69 1 0% NA NA% NA NA%
\label{k69-topic-pyramid-pllot}K69 Pyramid Chart

K69 Pyramid Chart

\label{k69-topic-bar-plot}K69 Bar Chart

K69 Bar Chart

3.3.1.1 Word Occurences

The table below shows the ten most common words in each topic, and the ten words with the highest FREX score, a measure that uses a harmonic mean of word exclusivity and topic coherence (Airoldi & Bischof, 2016).

Words in topic - K69
Topic Number Top Ten Words Top Ten FREX
Topic 1 secretary, state, tell, ministers, given, today, department, can, confirm, said secretary, state, confirm, tell, ministers, state’s, minister’s, explain, please, discussions
Topic 2 safety, register, registration, indicated, registered, electoral, risk, risks, number, individual registration, indicated, hse, canvass, register, gurkhas, safety, dissent, hare, trustee
Topic 3 make, sure, statement, progress, difference, northern, ireland, towards, representations, responsibilities statement, make, sure, progress, ireland, representations, difference, northern, milton, departmental
Topic 4 debt, water, credit, charges, pay, loan, loans, people, financial, cost payday, loan, lenders, debts, loans, debt, charges, water, high-cost, creditors
Topic 5 house, committee, parliament, leader, select, motion, parliamentary, debate, scrutiny, business select, leader, house, motion, committee, backbench, scrutiny, committees, benchers, parliamentary
Topic 6 new, development, work, need, investment, strategy, must, programme, working, also development, strategy, develop, project, regional, projects, partnership, together, developed, build
Topic 7 road, petition, residents, car, vehicles, petitioners, dogs, roads, site, house petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling, declares, drivers, accidents
Topic 8 important, agree, welcome, country, making, particularly, thank, part, makes, good agree, welcome, important, absolutely, makes, making, friend’s, thank, particularly, giving
Topic 9 companies, market, company, competition, energy, consumers, prices, price, consumer, customers competition, companies, market, wholesale, suppliers, company, regulator, ofgem, supplier, consumers
Topic 10 women, men, equality, women’s, discrimination, rights, gender, equal, woman, marriage gender, bishops, transgender, women’s, women, abortion, same-sex, marriage, equality, gay
Topic 11 energy, climate, fuel, change, green, carbon, emissions, gas, environmental, industry renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide, kyoto, carbon, climate
Topic 12 office, post, offices, royal, service, closure, mail, services, network, christmas offices, mail, sub-post, post, sub-postmasters, closures, consignia, swindon, closure, office
Topic 13 mr, north, south, east, west, spoke, friends, birmingham, talked, central ealing, spoke, dorset, lothian, ayrshire, glasgow, chris, southwark, pontefract, birmingham
Topic 14 pension, scheme, benefit, pensions, benefits, pensioners, system, credit, allowance, income pension, esa, pensions, claimants, retirement, pip, pensioners, incapacity, dwp, means-testing
Topic 15 economy, jobs, economic, growth, unemployment, country, investment, chancellor, budget, crisis unemployment, recession, growth, economy, obr, deficit, inflation, economic, forecast, borrowing
Topic 16 schools, school, education, children, teachers, parents, pupils, educational, special, primary academies, pupil, grammar, schools, pupils, teachers, ofsted, school, teacher, sen
Topic 17 want, say, one, think, know, need, us, get, go, see think, say, things, want, something, saying, going, lot, really, go
Topic 18 review, report, commission, independent, process, recommendations, inquiry, also, system, standards recommendations, inquiry, panel, audit, independent, recommendation, reviews, fsa, complaints, review
Topic 19 business, businesses, small, financial, bank, banks, insurance, rates, industry, enterprise smes, medium-sized, businesses, bank, enterprises, enterprise, banking, rbs, business, rock
Topic 20 wales, industry, welsh, north-east, england, assembly, constituency, jobs, manufacturing, uk welsh, wales, steel, cardiff, north-east, assembly, visteon, newcastle, manufacturing, tyneside
Topic 21 care, services, social, mental, need, health, home, provision, service, older mental, care, social, elderly, older, advocacy, services, residential, palliative, discharges
Topic 22 pay, work, workers, employment, working, wage, minimum, employers, paid, national wage, workers, zero-hours, employees, paternity, employer, minimum, employers, employment, workplace
Topic 23 amendment, clause, amendments, new, 1, lords, section, 2, act, clauses amendment, nos, insert, subsection, clause, amendments, clauses, section, lords, schedule
Topic 24 report, last, since, said, received, published, year, following, official, end march, vol, official, january, july, november, published, december, june, october
Topic 25 made, clear, impact, decision, changes, recent, assessment, decisions, effect, proposed made, decision, assessment, clear, decisions, impact, implications, recent, changes, effect
Topic 26 funding, cuts, fund, cut, budget, grant, spending, bbc, review, flood flood, funding, bbc, formula, grant, flooding, floods, cumbria, lottery, grants
Topic 27 money, spent, extra, spend, liberal, cost, spending, value, opposition, tory money, spent, liberal, spend, democrats, tories, tory, lib, democrat, conservatives
Topic 28 constituency, great, community, proud, many, sport, one, also, world, new maiden, arts, football, museum, museums, sport, olympic, games, sports, heritage
Topic 29 families, child, poverty, children, parents, work, credit, working, family, living lone, poverty, childcare, families, low-income, child, nursery, four-year-olds, nurseries, joseph
Topic 30 party, conservative, vote, parliament, political, election, labour, parties, scottish, elected party, vote, voting, conservative, party’s, voters, election, voted, votes, politics
Topic 31 point, can, may, issue, take, however, whether, matter, understand, consider matter, point, understand, consider, certainly, accept, possible, issue, course, happy
Topic 32 member, said, lady, mentioned, raised, comments, speech, referred, points, remarks member, lady, comments, remarks, bromley, interesting, chislehurst, pointed, front-bench, mentioned
Topic 33 european, uk, eu, countries, united, union, europe, states, british, trade accession, enlargement, wto, lisbon, treaty, eu, doha, european, negotiations, brexit
Topic 34 education, skills, young, training, students, university, college, higher, science, apprenticeships ema, fe, students, apprenticeship, universities, qualifications, apprenticeships, graduates, vocational, courses
Topic 35 local, authorities, authority, planning, community, communities, councils, area, guidance, system authorities, local, authority, planning, councils, councillors, locally, guidance, localism, communities
Topic 36 disabled, carers, disability, support, disabilities, needs, caring, autism, learning, can carers, autism, autistic, disabled, disabilities, disability, dementia, carer, caring, deaf
Topic 37 environment, marine, fishing, sea, industry, natural, fish, countryside, rural, fisheries fishermen, cod, forestry, biodiversity, habitats, mmo, fishing, fish, cfp, fisheries
Topic 38 justice, court, violence, victims, cases, criminal, domestic, courts, prison, offence attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking, prosecutor, prisons, prosecution
Topic 39 international, foreign, rights, human, peace, un, conflict, world, aid, war israel, palestinian, israeli, gaza, sri, zimbabwe, iran, yemen, hamas, palestinians
Topic 40 day, family, never, told, families, life, happened, constituent, man, went man, died, son, story, stories, hillsborough, tragedy, daughter, husband, angry
Topic 41 proposals, future, forward, consultation, plans, meet, paper, current, discuss, bring proposals, consultation, paper, plans, forward, discuss, white, proposal, meet, implement
Topic 42 behaviour, crime, antisocial, alcohol, young, drugs, drug, problem, use, tackle antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking, fireworks, behaviour, graffiti
Topic 43 housing, homes, social, affordable, private, home, accommodation, rent, need, properties housing, tenants, rented, tenancies, homelessness, leasehold, landlords, rents, properties, leaseholders
Topic 44 question, order, mr, put, asked, answer, questions, ask, speaker, time question, answer, questions, speaker, asked, deputy, answers, order, apologise, read
Topic 45 research, cancer, treatment, medical, condition, screening, disease, can, patients, use embryos, prostate, cervical, hepatitis, cloning, transplant, embryo, fertilisation, embryonic, endometriosis
Topic 46 online, internet, farmers, animals, digital, animal, broadband, sites, tickets, technology cull, badgers, badger, fur, bovine, mink, culling, circuses, touts, snares
Topic 47 defence, forces, armed, plymouth, personnel, service, military, army, nuclear, royal mod, naval, hms, submarines, dockyard, veterans, armed, plymouth, covenant, personnel
Topic 48 information, home, security, data, immigration, control, orders, system, terrorism, appeal extradition, tpims, sia, warrant, detention, checks, tpim, terrorism, intercept, identity
Topic 49 police, officers, crime, policing, home, force, service, forces, officer, chief constable, constables, officers, policing, police, soca, ipcc, constabulary, pcsos, hmic
Topic 50 nhs, hospital, patients, health, services, hospitals, care, service, trust, trusts dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital, dental, trusts, patients
Topic 51 tax, budget, cut, chancellor, cuts, rate, income, vat, benefit, hit 50p, vat, millionaires, hit, tax, allowances, credits, richest, chancellor, ifs
Topic 52 years, now, two, time, first, three, past, one, months, ago years, three, months, ago, two, past, weeks, five, four, now
Topic 53 staff, doctors, emergency, medical, service, training, nurses, royal, junior, ambulance ambulance, junior, staffing, doctors, halifax, posts, nurses, fss, staff, cpr
Topic 54 bill, legislation, act, law, rights, provisions, powers, regulations, place, believe bill, legislation, bill’s, provisions, passage, regulations, legislative, draft, statute, definition
Topic 55 public, sector, private, organisations, service, voluntary, services, society, community, organisation public, voluntary, organisations, sector, private, co-operative, volunteering, volunteers, volunteer, co-operatives
Topic 56 health, national, inequalities, programme, suicide, disease, department, prevention, among, risk flu, hiv, pandemic, inequalities, infections, suicide, mortality, infection, mrsa, vaccine
Topic 57 council, london, areas, city, area, constituency, centre, rural, county, liverpool county, mayor, borough, cities, liverpool, city, regeneration, council’s, london, towns
Topic 58 advice, legal, cases, civil, hull, aid, case, compensation, claims, service hull, tribunal, legal, compensation, solicitors, advice, concentrix, servants, lawyers, tribunals
Topic 59 people, work, many, young, get, people’s, can, help, lives, job people, people’s, get, getting, work, young, jobcentre, lives, youth, find
Topic 60 tax, revenue, relief, duty, uk, avoidance, hmrc, charities, companies, taxation evasion, hmrc, gaar, avoidance, inland, stamp, revenue, relief, gift, dependencies
Topic 61 government, government’s, policy, labour, previous, scotland, scottish, commitment, policies, coalition government, previous, policy, government’s, scotland, coalition, scottish, labour, disappointing, administrations
Topic 62 trafficking, home, uk, asylum, refugees, immigration, country, human, migration, britain trafficking, slavery, trafficked, sierra, leone, slave, dubs, fgm, yarl’s, wilberforce
Topic 63 food, products, industry, smoking, advertising, tobacco, ban, product, standards, shops gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets, labelling, retailers, packaging
Topic 64 members, debate, many, issues, also, today, heard, opportunity, hope, issue members, debate, heard, speak, sides, issues, hear, opportunity, listened, pleased
Topic 65 children, child, parents, young, children’s, family, contact, vulnerable, adoption, abuse csa, adopters, adoption, child’s, cafcass, looked-after, children’s, children, safeguarding, barred
Topic 66 transport, rail, bus, services, line, travel, train, network, passengers, london rail, passengers, passenger, heathrow, hs2, freight, high-speed, crossrail, airlines, runway
Topic 67 year, million, number, increase, figures, increased, billion, 1, average, cost million, figures, figure, increased, increase, compared, year, total, fallen, estimates
Topic 68 support, ensure, can, help, aware, taking, take, provide, action, continue aware, ensure, support, taking, steps, continue, help, action, assure, encourage
Topic 69 deal, recently, new, can, lack, great, concern, done, move, given deal, recently, lack, elsewhere, concern, great, improved, offered, done, new

3.4 Full topic model summary - K69

## A topic model with 69 topics, 81607 documents and a 115477 word dictionary.
## Topic 1 Top Words:
##       Highest Prob: secretary, state, tell, ministers, given, today, department 
##       FREX: secretary, state, confirm, tell, ministers, state's, minister's 
##       Lift: dhar, lowell, qatada's, #nationalistsconfused, 1135, 2.5bn, 36,500 
##       Score: secretary, state, confirm, state's, tell, ministers, department 
## Topic 2 Top Words:
##       Highest Prob: safety, register, registration, indicated, registered, electoral, risk 
##       FREX: registration, indicated, hse, canvass, register, gurkhas, safety 
##       Lift: aps, hse, 10-litre, 13a, 14,940, 1760, 1867 
##       Score: safety, registration, register, electoral, indicated, registered, hse 
## Topic 3 Top Words:
##       Highest Prob: make, sure, statement, progress, difference, northern, ireland 
##       FREX: statement, make, sure, progress, ireland, representations, difference 
##       Lift: 101269, 101548, 102938, 103414, 106583, 107305, 109413 
##       Score: make, statement, progress, sure, ireland, northern, milton 
## Topic 4 Top Words:
##       Highest Prob: debt, water, credit, charges, pay, loan, loans 
##       FREX: payday, loan, lenders, debts, loans, debt, charges 
##       Lift: 1,021, 1,025, 1,106, 1,189, 1,273, 1,385, 1,413 
##       Score: debt, water, payday, loan, loans, lenders, credit 
## Topic 5 Top Words:
##       Highest Prob: house, committee, parliament, leader, select, motion, parliamentary 
##       FREX: select, leader, house, motion, committee, backbench, scrutiny 
##       Lift: e-petitions, praying-against, sherlock, wednesdays, sittings, thursdays, 10,000-signature 
##       Score: committee, house, leader, select, scrutiny, parliament, motion 
## Topic 6 Top Words:
##       Highest Prob: new, development, work, need, investment, strategy, must 
##       FREX: development, strategy, develop, project, regional, projects, partnership 
##       Lift: #1,150, 1.245, 1.875, 128406, 131,000, 18-the, 2002-around 
##       Score: development, regional, investment, strategy, infrastructure, projects, work 
## Topic 7 Top Words:
##       Highest Prob: road, petition, residents, car, vehicles, petitioners, dogs 
##       FREX: petitioners, lap-dancing, petition, dogs, dog, pedestrians, cycling 
##       Lift: 0.037, 0.044, 0fficial, 1,042, 1,072, 1,108, 1,122 
##       Score: petitioners, petition, dogs, road, residents, dog, declares 
## Topic 8 Top Words:
##       Highest Prob: important, agree, welcome, country, making, particularly, thank 
##       FREX: agree, welcome, important, absolutely, makes, making, friend's 
##       Lift: adequacies, and-importantly-much, ayse, ballantine, bobtail-and, bostrom, bucketfuls 
##       Score: agree, important, thank, welcome, friend's, absolutely, country 
## Topic 9 Top Words:
##       Highest Prob: companies, market, company, competition, energy, consumers, prices 
##       FREX: competition, companies, market, wholesale, suppliers, company, regulator 
##       Lift: 1,105, 1,345, ashington, boakye, nord, over-charging, price-fixing 
##       Score: companies, consumers, energy, market, company, prices, competition 
## Topic 10 Top Words:
##       Highest Prob: women, men, equality, women's, discrimination, rights, gender 
##       FREX: gender, bishops, transgender, women's, women, abortion, same-sex 
##       Lift: balmforth, bpas, celebrants, cohabitants, jessy, msafiri, natal 
##       Score: women, women's, equality, men, gender, discrimination, marriage 
## Topic 11 Top Words:
##       Highest Prob: energy, climate, fuel, change, green, carbon, emissions 
##       FREX: renewables, solar, insulation, feed-in, biofuels, greenhouse, dioxide 
##       Lift: fossil, #2,500, #solar, 1-are, 1,129, 1,214, 1,343 
##       Score: energy, fuel, carbon, emissions, climate, renewable, renewables 
## Topic 12 Top Words:
##       Highest Prob: office, post, offices, royal, service, closure, mail 
##       FREX: offices, mail, sub-post, post, sub-postmasters, closures, consignia 
##       Lift: #1.8, #210, #450, 1,001, 1,352, 1,639, 1,827 
##       Score: post, offices, office, mail, closure, postal, sub-post 
## Topic 13 Top Words:
##       Highest Prob: mr, north, south, east, west, spoke, friends 
##       FREX: ealing, spoke, dorset, lothian, ayrshire, glasgow, chris 
##       Lift: argos's, blackford, cairns's, clemency, marxist, no2id, 0.66 
##       Score: mr, east, north, south, west, spoke, birmingham 
## Topic 14 Top Words:
##       Highest Prob: pension, scheme, benefit, pensions, benefits, pensioners, system 
##       FREX: pension, esa, pensions, claimants, retirement, pip, pensioners 
##       Lift: means-testing, #20,000, #400, 0º, 1,052, 1,366, 1,482 
##       Score: pension, pensions, pensioners, allowance, scheme, retirement, credit 
## Topic 15 Top Words:
##       Highest Prob: economy, jobs, economic, growth, unemployment, country, investment 
##       FREX: unemployment, recession, growth, economy, obr, deficit, inflation 
##       Lift: 0.76, 0.83, 1,196, 1,319, 1.04, 1.32, 10-about 
##       Score: economy, jobs, unemployment, growth, economic, recession, chancellor 
## Topic 16 Top Words:
##       Highest Prob: schools, school, education, children, teachers, parents, pupils 
##       FREX: academies, pupil, grammar, schools, pupils, teachers, ofsted 
##       Lift: sen2, 11-plus, leas, pupil, 000-to, 1-regardless, 1,000-pupil 
##       Score: schools, school, teachers, pupils, children, education, parents 
## Topic 17 Top Words:
##       Highest Prob: want, say, one, think, know, need, us 
##       FREX: think, say, things, want, something, saying, going 
##       Lift: about-part, arguing-i, beneficial-we, career-many, cause-the, clam, compatriot 
##       Score: think, want, get, say, things, going, us 
## Topic 18 Top Words:
##       Highest Prob: review, report, commission, independent, process, recommendations, inquiry 
##       FREX: recommendations, inquiry, panel, audit, independent, recommendation, reviews 
##       Lift: bourn, cag's, clarke's, clowes, dg, emag, equitable's 
##       Score: fsa, inquiry, review, commission, recommendations, report, independent 
## Topic 19 Top Words:
##       Highest Prob: business, businesses, small, financial, bank, banks, insurance 
##       FREX: smes, medium-sized, businesses, bank, enterprises, enterprise, banking 
##       Lift: 0.21, 0.84, 1,034, 1,130, 10-fold, 1130, 12.19 
##       Score: businesses, business, bank, banks, banking, insurance, small 
## Topic 20 Top Words:
##       Highest Prob: wales, industry, welsh, north-east, england, assembly, constituency 
##       FREX: welsh, wales, steel, cardiff, north-east, assembly, visteon 
##       Lift: a55, angharad, bethesda, co-investment, dewhirst, dogger, gorge 
##       Score: wales, welsh, assembly, manufacturing, steel, north-east, yorkshire 
## Topic 21 Top Words:
##       Highest Prob: care, services, social, mental, need, health, home 
##       FREX: mental, care, social, elderly, older, advocacy, services 
##       Lift: #900,000, 1,051, 1,312, 10,758, 104962, 1089, 13,198 
##       Score: care, mental, services, social, health, older, homes 
## Topic 22 Top Words:
##       Highest Prob: pay, work, workers, employment, working, wage, minimum 
##       FREX: wage, workers, zero-hours, employees, paternity, employer, minimum 
##       Lift: 6.70, 8.20, 85p, awb, e-balloting, hannett, increments 
##       Score: wage, workers, employers, employment, pay, employees, minimum 
## Topic 23 Top Words:
##       Highest Prob: amendment, clause, amendments, new, 1, lords, section 
##       FREX: amendment, nos, insert, subsection, clause, amendments, clauses 
##       Lift: 153a, 22a, 287, 50b, 51b, counter-notice, insured's 
##       Score: clause, amendment, amendments, lords, nos, insert, subsection 
## Topic 24 Top Words:
##       Highest Prob: report, last, since, said, received, published, year 
##       FREX: march, vol, official, january, july, november, published 
##       Lift: 1-2ws, 1,033, 1,099, 1,124,818, 1,337, 1,368,186, 1,595 
##       Score: report, official, vol, published, march, april, november 
## Topic 25 Top Words:
##       Highest Prob: made, clear, impact, decision, changes, recent, assessment 
##       FREX: made, decision, assessment, clear, decisions, impact, implications 
##       Lift: 104963, 107312, 116,400, 125214, 125828, 125830, 126370 
##       Score: made, assessment, impact, changes, decision, decisions, clear 
## Topic 26 Top Words:
##       Highest Prob: funding, cuts, fund, cut, budget, grant, spending 
##       FREX: flood, funding, bbc, formula, grant, flooding, floods 
##       Lift: #10.89, #12, #3.3, #bbcdiversity, 1,027, 1,536-will, 1,546 
##       Score: funding, cuts, flood, bbc, budget, spending, flooding 
## Topic 27 Top Words:
##       Highest Prob: money, spent, extra, spend, liberal, cost, spending 
##       FREX: money, spent, liberal, spend, democrats, tories, tory 
##       Lift: 1-but, 10,309.63, 1228, 158.8, 1763, 18-that, 1979-80 
##       Score: money, liberal, tory, democrats, conservatives, tories, spending 
## Topic 28 Top Words:
##       Highest Prob: constituency, great, community, proud, many, sport, one 
##       FREX: maiden, arts, football, museum, museums, sport, olympic 
##       Lift: 0.27, 0.51, 1,084, 1,126, 1,468, 1,580-plus, 1,983 
##       Score: arts, sport, museum, maiden, heritage, football, constituency 
## Topic 29 Top Words:
##       Highest Prob: families, child, poverty, children, parents, work, credit 
##       FREX: lone, poverty, childcare, families, low-income, child, nursery 
##       Lift: 1,000-discriminates, 1,000-not, 1,080, 1,142,600, 1,170, 1,390, 1,664 
##       Score: poverty, child, families, children, parents, credit, lone 
## Topic 30 Top Words:
##       Highest Prob: party, conservative, vote, parliament, political, election, labour 
##       FREX: party, vote, voting, conservative, party's, voters, election 
##       Lift: alphabetical, gentry, olga, one-party, randomisation, 1,166, 1,294 
##       Score: party, conservative, vote, scottish, election, elections, political 
## Topic 31 Top Words:
##       Highest Prob: point, can, may, issue, take, however, whether 
##       FREX: matter, point, understand, consider, certainly, accept, possible 
##       Lift: 450,000-in, advised-by, backslid, bill-albeit, bizarre-but, can-enable, cases-roughly 
##       Score: point, matter, issue, gentleman's, consider, shall, whether 
## Topic 32 Top Words:
##       Highest Prob: member, said, lady, mentioned, raised, comments, speech 
##       FREX: member, lady, comments, remarks, bromley, interesting, chislehurst 
##       Lift: 12.20, 130b, 1991-forcing, 2,784, 54,000-worth, achieved-is, arguments-and 
##       Score: member, lady, comments, said, speech, raised, points 
## Topic 33 Top Words:
##       Highest Prob: european, uk, eu, countries, united, union, europe 
##       FREX: accession, enlargement, wto, lisbon, treaty, eu, doha 
##       Lift: 13652, 13653, 13654, 1707, balkan, barnier, blackmailing 
##       Score: eu, european, countries, union, treaty, europe, trade 
## Topic 34 Top Words:
##       Highest Prob: education, skills, young, training, students, university, college 
##       FREX: ema, fe, students, apprenticeship, universities, qualifications, apprenticeships 
##       Lift: ema, #ne, 1,188, 1,308, 1,555-what, 1,740, 1,803 
##       Score: students, education, young, skills, apprenticeships, training, universities 
## Topic 35 Top Words:
##       Highest Prob: local, authorities, authority, planning, community, communities, councils 
##       FREX: authorities, local, authority, planning, councils, councillors, locally 
##       Lift: achcew, central-local, laa, lsp, maas, observances, place-shaping 
##       Score: local, authorities, authority, councils, planning, communities, community 
## Topic 36 Top Words:
##       Highest Prob: disabled, carers, disability, support, disabilities, needs, caring 
##       FREX: carers, autism, autistic, disabled, disabilities, disability, dementia 
##       Lift: autism, rnib, #185, #85, #hellomynameis, 1,400-one, 10-person 
##       Score: carers, disabled, disability, autism, disabilities, caring, dementia 
## Topic 37 Top Words:
##       Highest Prob: environment, marine, fishing, sea, industry, natural, fish 
##       FREX: fishermen, cod, forestry, biodiversity, habitats, mmo, fishing 
##       Lift: aquaculture, arable, bee-friendly, biodiversity, birdlife, bycatch, caterpillar 
##       Score: marine, fishing, fishermen, fish, fisheries, wildlife, conservation 
## Topic 38 Top Words:
##       Highest Prob: justice, court, violence, victims, cases, criminal, domestic 
##       FREX: attorney-general, defendants, defendant, prison, prosecutors, solicitor-general, stalking 
##       Lift: #9, 0.08, 0.48, 1,046, 1,237, 10,544, 10.15 
##       Score: violence, prison, court, offence, criminal, rape, victims 
## Topic 39 Top Words:
##       Highest Prob: international, foreign, rights, human, peace, un, conflict 
##       FREX: israel, palestinian, israeli, gaza, sri, zimbabwe, iran 
##       Lift: lankan, saddam, #aleppo, 1,010, 1,476, 1,591, 1224 
##       Score: un, israel, syria, humanitarian, palestinian, israeli, iraq 
## Topic 40 Top Words:
##       Highest Prob: day, family, never, told, families, life, happened 
##       FREX: man, died, son, story, stories, hillsborough, tragedy 
##       Lift: 10,000-seat, 12-inch, 1234, 1519-20, 1635, 1710, 174,995 
##       Score: families, holocaust, family, constituent, man, died, mother 
## Topic 41 Top Words:
##       Highest Prob: proposals, future, forward, consultation, plans, meet, paper 
##       FREX: proposals, consultation, paper, plans, forward, discuss, white 
##       Lift: 10.42, 107910, 109648, 114061, 119621, 141605, 141607 
##       Score: proposals, consultation, plans, future, forward, paper, white 
## Topic 42 Top Words:
##       Highest Prob: behaviour, crime, antisocial, alcohol, young, drugs, drug 
##       FREX: antisocial, asbos, alcohol, alcohol-related, binge, psychoactive, drinking 
##       Lift: acquisitive, addaction, auto, bailes, crawlers, ghb, gilpin 
##       Score: antisocial, crime, behaviour, alcohol, drug, drugs, cannabis 
## Topic 43 Top Words:
##       Highest Prob: housing, homes, social, affordable, private, home, accommodation 
##       FREX: housing, tenants, rented, tenancies, homelessness, leasehold, landlords 
##       Lift: one-for-one, one-bedroom, rented, right-to-buy, #19, #21.5, #28.5 
##       Score: housing, homes, tenants, rented, rent, landlords, affordable 
## Topic 44 Top Words:
##       Highest Prob: question, order, mr, put, asked, answer, questions 
##       FREX: question, answer, questions, speaker, asked, deputy, answers 
##       Lift: 11.00, 11.57, 12.26, 1223, 1232, 1412, 1555-56 
##       Score: question, speaker, mr, answer, deputy, order, questions 
## Topic 45 Top Words:
##       Highest Prob: research, cancer, treatment, medical, condition, screening, disease 
##       FREX: embryos, prostate, cervical, hepatitis, cloning, transplant, embryo 
##       Lift: abnormalities, cystic, embryo, fertilisation, marrow, @cfaware, #500 
##       Score: cancer, patients, embryos, screening, treatment, tissue, breast 
## Topic 46 Top Words:
##       Highest Prob: online, internet, farmers, animals, digital, animal, broadband 
##       FREX: cull, badgers, badger, fur, bovine, mink, culling 
##       Lift: culling, @daisydumble, @donna_smiley, @jimspin, @leamingtonsbc, @maggieannehayes, @nhconsortium 
##       Score: farmers, animals, internet, cull, animal, online, badgers 
## Topic 47 Top Words:
##       Highest Prob: defence, forces, armed, plymouth, personnel, service, military 
##       FREX: mod, naval, hms, submarines, dockyard, veterans, armed 
##       Lift: hms, submarine, submarines, 1,000-people, 1,625, 1,705, 10-3 
##       Score: defence, armed, forces, plymouth, military, personnel, mod 
## Topic 48 Top Words:
##       Highest Prob: information, home, security, data, immigration, control, orders 
##       FREX: extradition, tpims, sia, warrant, detention, checks, tpim 
##       Lift: carlile's, carlile, sia, 10-month, 10,410, 10,500-for, 10.45 
##       Score: immigration, terrorism, detention, terrorist, tpims, home, security 
## Topic 49 Top Words:
##       Highest Prob: police, officers, crime, policing, home, force, service 
##       FREX: constable, constables, officers, policing, police, soca, ipcc 
##       Lift: 2003-morecambe, 9,650, a19s, ashleys, bigg, bounties, ckp 
##       Score: police, officers, policing, crime, forces, constable, neighbourhood 
## Topic 50 Top Words:
##       Highest Prob: nhs, hospital, patients, health, services, hospitals, care 
##       FREX: dentists, dentistry, pharmacies, pct, nhs, hospitals, hospital 
##       Lift: acos, bequest, bernstein, bodmin, catto, cayton, chailey 
##       Score: nhs, patients, hospital, health, patient, hospitals, care 
## Topic 51 Top Words:
##       Highest Prob: tax, budget, cut, chancellor, cuts, rate, income 
##       FREX: 50p, vat, millionaires, hit, tax, allowances, credits 
##       Lift: #840, 0.76p, 1,003, 1,009, 1,226, 1,275, 1,296 
##       Score: tax, vat, budget, credits, chancellor, cuts, income 
## Topic 52 Top Words:
##       Highest Prob: years, now, two, time, first, three, past 
##       FREX: years, three, months, ago, two, past, weeks 
##       Lift: 10-week-old, 10,616, 11-month, 11-point, 1758, 196b, 63,500 
##       Score: years, months, two, ago, three, past, weeks 
## Topic 53 Top Words:
##       Highest Prob: staff, doctors, emergency, medical, service, training, nurses 
##       FREX: ambulance, junior, staffing, doctors, halifax, posts, nurses 
##       Lift: #i'm, 03, 1-who, 1,454, 1,631, 10,000-strong, 10,000-with 
##       Score: staff, doctors, ambulance, nurses, medical, emergency, junior 
## Topic 54 Top Words:
##       Highest Prob: bill, legislation, act, law, rights, provisions, powers 
##       FREX: bill, legislation, bill's, provisions, passage, regulations, legislative 
##       Lift: 1865, 1990s-when, 1998-it, 19may2000, 2003-largely, 2005-have, 29-year 
##       Score: bill, legislation, provisions, rights, law, powers, regulations 
## Topic 55 Top Words:
##       Highest Prob: public, sector, private, organisations, service, voluntary, services 
##       FREX: public, voluntary, organisations, sector, private, co-operative, volunteering 
##       Lift: 1844, af, carpetbaggers, nebulous, puk, 1075, 170-year 
##       Score: public, sector, private, voluntary, organisations, service, services 
## Topic 56 Top Words:
##       Highest Prob: health, national, inequalities, programme, suicide, disease, department 
##       FREX: flu, hiv, pandemic, inequalities, infections, suicide, mortality 
##       Lift: kirkley, lowestoft, nihr, acupuncture, influenza, #148, #3.6 
##       Score: health, vaccine, flu, inequalities, hiv, infection, suicide 
## Topic 57 Top Words:
##       Highest Prob: council, london, areas, city, area, constituency, centre 
##       FREX: county, mayor, borough, cities, liverpool, city, regeneration 
##       Lift: #12,000, #14.4, #356, #38, #5,000, #500,000, #66.6 
##       Score: london, council, city, regeneration, county, rural, borough 
## Topic 58 Top Words:
##       Highest Prob: advice, legal, cases, civil, hull, aid, case 
##       FREX: hull, tribunal, legal, compensation, solicitors, advice, concentrix 
##       Lift: 0300, 1,997, 1.148, 1.4m, 112.8, 1147, 128,687 
##       Score: legal, advice, hull, aid, compensation, civil, tribunal 
## Topic 59 Top Words:
##       Highest Prob: people, work, many, young, get, people's, can 
##       FREX: people, people's, get, getting, work, young, jobcentre 
##       Lift: 2,425, 294,488, 3,699, 37,290, 5,320, 50-to-64, 75589 
##       Score: people, young, work, get, youth, many, people's 
## Topic 60 Top Words:
##       Highest Prob: tax, revenue, relief, duty, uk, avoidance, hmrc 
##       FREX: evasion, hmrc, gaar, avoidance, inland, stamp, revenue 
##       Lift: 1,643, 3.12, 32.2, 44a, 80g, aaronson, aat 
##       Score: tax, hmrc, avoidance, revenue, relief, evasion, territories 
## Topic 61 Top Words:
##       Highest Prob: government, government's, policy, labour, previous, scotland, scottish 
##       FREX: government, previous, policy, government's, scotland, coalition, scottish 
##       Lift: 2005-perhaps, 80994, actually-that, agency-when, agenda-access, alloway, aware-in 
##       Score: government, scotland, scottish, labour, policy, government's, previous 
## Topic 62 Top Words:
##       Highest Prob: trafficking, home, uk, asylum, refugees, immigration, country 
##       FREX: trafficking, slavery, trafficked, sierra, leone, slave, dubs 
##       Lift: #7, 0.025, 1-yes, 1,060, 1,483, 1,746, 1.123 
##       Score: trafficking, refugees, asylum, slavery, trafficked, immigration, sierra 
## Topic 63 Top Words:
##       Highest Prob: food, products, industry, smoking, advertising, tobacco, ban 
##       FREX: gambling, betting, sunbed, tobacco, cocoa, meat, supermarkets 
##       Lift: 0.7p, 00, 0157, 1,000-almost, 1,032, 1,200-i, 1,666 
##       Score: food, smoking, products, tobacco, advertising, gambling, industry 
## Topic 64 Top Words:
##       Highest Prob: members, debate, many, issues, also, today, heard 
##       FREX: members, debate, heard, speak, sides, issues, hear 
##       Lift: noakes, 6.47pm, accept-or, analysis-but, aspect-listening-is, bunfight, called-making 
##       Score: members, debate, issues, many, opposition, heard, constituents 
## Topic 65 Top Words:
##       Highest Prob: children, child, parents, young, children's, family, contact 
##       FREX: csa, adopters, adoption, child's, cafcass, looked-after, children's 
##       Lift: csa, @mandatenow, 10-month-old, 10-went, 12j, 150765, 16-only 
##       Score: children, child, parents, young, children's, adoption, child's 
## Topic 66 Top Words:
##       Highest Prob: transport, rail, bus, services, line, travel, train 
##       FREX: rail, passengers, passenger, heathrow, hs2, freight, high-speed 
##       Lift: 12-car, 15.15, 50.1, adtranz, anti-icing, bahn, chilterns 
##       Score: rail, transport, bus, passengers, fares, trains, hs2 
## Topic 67 Top Words:
##       Highest Prob: year, million, number, increase, figures, increased, billion 
##       FREX: million, figures, figure, increased, increase, compared, year 
##       Lift: #112, #3,850, #84.3, #87.2, 1,249, 102269, 122.9 
##       Score: million, year, billion, increase, figures, average, increased 
## Topic 68 Top Words:
##       Highest Prob: support, ensure, can, help, aware, taking, take 
##       FREX: aware, ensure, support, taking, steps, continue, help 
##       Lift: 103684, 103965, 107320, 111532, 112584, 113698, 117890 
##       Score: support, ensure, steps, aware, help, taking, department 
## Topic 69 Top Words:
##       Highest Prob: deal, recently, new, can, lack, great, concern 
##       FREX: deal, recently, lack, elsewhere, concern, great, improved 
##       Lift: 2004-a, 721,000, added-gva-per, age-of, centres-jacs-which, employment-can, index-the 
##       Score: deal, recently, new, worktrack, lack, can, great

3.5 Full topic model estimate summary - K69

## 
## Call:
## estimateEffect(formula = 1:69 ~ short_list, stmobj = topic_model2, 
##     metadata = lab_corpus_fem_stm$meta, uncertainty = "Global")
## 
## 
## Topic 1:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0170435  0.0002879   59.20 <0.0000000000000002 ***
## short_listTRUE 0.0069026  0.0003496   19.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 2:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0058955  0.0002420  24.358 <0.0000000000000002 ***
## short_listTRUE 0.0007062  0.0003050   2.315              0.0206 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 3:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0087144  0.0001127  77.309 <0.0000000000000002 ***
## short_listTRUE 0.0002131  0.0001475   1.445               0.148    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 4:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0064743  0.0003071   21.08 <0.0000000000000002 ***
## short_listTRUE 0.0036202  0.0003918    9.24 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 5:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0120624  0.0002519   47.88 <0.0000000000000002 ***
## short_listTRUE 0.0035421  0.0003303   10.72 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 6:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0313831  0.0004291   73.14 <0.0000000000000002 ***
## short_listTRUE -0.0093831  0.0004957  -18.93 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 7:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0081985  0.0003833  21.390 < 0.0000000000000002 ***
## short_listTRUE 0.0024229  0.0004801   5.047           0.00000045 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 8:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0155977  0.0001354 115.222 < 0.0000000000000002 ***
## short_listTRUE -0.0006674  0.0001470  -4.542           0.00000559 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 9:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0106919  0.0002692  39.718 < 0.0000000000000002 ***
## short_listTRUE -0.0025219  0.0003483  -7.241     0.00000000000045 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 10:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0147989  0.0004500  32.885 <0.0000000000000002 ***
## short_listTRUE 0.0002626  0.0005572   0.471               0.637    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 11:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0131078  0.0004357  30.084 <0.0000000000000002 ***
## short_listTRUE -0.0009014  0.0005203  -1.733              0.0832 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 12:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0100322  0.0003276   30.62 < 0.0000000000000002 ***
## short_listTRUE -0.0026446  0.0003762   -7.03     0.00000000000208 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 13:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0084706  0.0002257  37.532 < 0.0000000000000002 ***
## short_listTRUE 0.0015075  0.0002922   5.159          0.000000249 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 14:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0133846  0.0004574   29.26 <0.0000000000000002 ***
## short_listTRUE 0.0060865  0.0005480   11.11 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 15:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0169555  0.0004249  39.905 < 0.0000000000000002 ***
## short_listTRUE 0.0029760  0.0005174   5.752        0.00000000883 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 16:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0135136  0.0005241  25.786 < 0.0000000000000002 ***
## short_listTRUE 0.0032587  0.0006744   4.832           0.00000135 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 17:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0425046  0.0003183 133.546 <0.0000000000000002 ***
## short_listTRUE 0.0008147  0.0003910   2.084              0.0372 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 18:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0268609  0.0004991   53.82 <0.0000000000000002 ***
## short_listTRUE -0.0068311  0.0006006  -11.37 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 19:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0129066  0.0003249  39.726 < 0.0000000000000002 ***
## short_listTRUE -0.0016947  0.0003861  -4.389            0.0000114 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 20:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0086065  0.0003458   24.89 <0.0000000000000002 ***
## short_listTRUE 0.0060669  0.0004429   13.70 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 21:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0124514  0.0003124  39.855 < 0.0000000000000002 ***
## short_listTRUE -0.0011159  0.0003980  -2.804              0.00505 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 22:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0127297  0.0003157  40.326 <0.0000000000000002 ***
## short_listTRUE 0.0008318  0.0003993   2.083              0.0372 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 23:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0185756  0.0005551  33.463 < 0.0000000000000002 ***
## short_listTRUE -0.0027946  0.0006233  -4.484           0.00000735 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 24:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0138603  0.0002290  60.516 < 0.0000000000000002 ***
## short_listTRUE 0.0013402  0.0002851   4.702           0.00000259 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 25:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0111248  0.0001346  82.653 < 0.0000000000000002 ***
## short_listTRUE 0.0007890  0.0001684   4.686           0.00000279 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 26:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0098015  0.0002871   34.14 <0.0000000000000002 ***
## short_listTRUE 0.0037726  0.0003834    9.84 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 27:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0103138  0.0002167  47.587 < 0.0000000000000002 ***
## short_listTRUE 0.0009108  0.0002731   3.335             0.000852 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 28:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0111592  0.0004003  27.877 < 0.0000000000000002 ***
## short_listTRUE 0.0028078  0.0005067   5.541         0.0000000302 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 29:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0105108  0.0003591  29.266 <0.0000000000000002 ***
## short_listTRUE 0.0042226  0.0004472   9.442 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 30:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0169430  0.0004391   38.59 <0.0000000000000002 ***
## short_listTRUE 0.0007979  0.0005542    1.44                0.15    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 31:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0369173  0.0002220   166.3 <0.0000000000000002 ***
## short_listTRUE -0.0068129  0.0002827   -24.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 32:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0100393  0.0001339  74.960 <0.0000000000000002 ***
## short_listTRUE -0.0002587  0.0001601  -1.616               0.106    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 33:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0230283  0.0005077   45.36 <0.0000000000000002 ***
## short_listTRUE -0.0059535  0.0006388   -9.32 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 34:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0136827  0.0004462  30.662 <0.0000000000000002 ***
## short_listTRUE 0.0010979  0.0005552   1.978               0.048 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 35:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0198285  0.0003381   58.65 <0.0000000000000002 ***
## short_listTRUE -0.0061414  0.0003909  -15.71 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 36:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0061167  0.0003208   19.07 <0.0000000000000002 ***
## short_listTRUE 0.0041061  0.0004083   10.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 37:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0069657  0.0003196  21.794 < 0.0000000000000002 ***
## short_listTRUE 0.0015000  0.0004215   3.558             0.000373 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 38:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0226435  0.0005276   42.92 <0.0000000000000002 ***
## short_listTRUE -0.0073560  0.0006033  -12.19 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 39:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0205479  0.0006330  32.459 <0.0000000000000002 ***
## short_listTRUE 0.0011686  0.0008248   1.417               0.157    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 40:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0139287  0.0003563   39.09 <0.0000000000000002 ***
## short_listTRUE 0.0047449  0.0004501   10.54 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 41:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0107933  0.0001173   92.02 <0.0000000000000002 ***
## short_listTRUE -0.0014800  0.0001338  -11.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 42:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0150546  0.0004394  34.258 < 0.0000000000000002 ***
## short_listTRUE -0.0033632  0.0005466  -6.153       0.000000000766 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 43:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0140285  0.0004847  28.944 < 0.0000000000000002 ***
## short_listTRUE 0.0021713  0.0006060   3.583              0.00034 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 44:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0147160  0.0002666  55.195 < 0.0000000000000002 ***
## short_listTRUE 0.0016371  0.0003344   4.896          0.000000981 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 45:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0135446  0.0004531  29.893 < 0.0000000000000002 ***
## short_listTRUE -0.0036865  0.0005326  -6.922     0.00000000000448 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 46:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0067864  0.0003654   18.57 <0.0000000000000002 ***
## short_listTRUE 0.0042905  0.0004437    9.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 47:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0077671  0.0003507   22.14 < 0.0000000000000002 ***
## short_listTRUE 0.0027525  0.0004454    6.18       0.000000000644 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 48:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0225033  0.0005094   44.17 <0.0000000000000002 ***
## short_listTRUE -0.0077118  0.0005846  -13.19 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 49:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0155770  0.0004835  32.215 < 0.0000000000000002 ***
## short_listTRUE -0.0038810  0.0005926  -6.549      0.0000000000582 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 50:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0232448  0.0006509   35.71 <0.0000000000000002 ***
## short_listTRUE -0.0076653  0.0007471  -10.26 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 51:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0105760  0.0003567   29.65 <0.0000000000000002 ***
## short_listTRUE 0.0055916  0.0004514   12.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 52:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.01749013 0.00018623  93.915 <0.0000000000000002 ***
## short_listTRUE 0.00001905 0.00022924   0.083               0.934    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 53:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0107598  0.0003976  27.063 <0.0000000000000002 ***
## short_listTRUE 0.0003065  0.0004669   0.657               0.511    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 54:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0261048  0.0004279  61.000 <0.0000000000000002 ***
## short_listTRUE -0.0044993  0.0004925  -9.135 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 55:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0101880  0.0001856  54.899 <0.0000000000000002 ***
## short_listTRUE -0.0004804  0.0002437  -1.972              0.0487 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 56:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0114035  0.0003502  32.563 < 0.0000000000000002 ***
## short_listTRUE -0.0015375  0.0004365  -3.522             0.000428 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 57:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0129069  0.0003207  40.243 < 0.0000000000000002 ***
## short_listTRUE 0.0012919  0.0004120   3.136              0.00172 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 58:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0090850  0.0002448  37.113 < 0.0000000000000002 ***
## short_listTRUE 0.0015904  0.0003330   4.775            0.0000018 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 59:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0166458  0.0001923  86.552 < 0.0000000000000002 ***
## short_listTRUE 0.0007301  0.0002314   3.155              0.00161 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 60:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0100660  0.0003752  26.831 <0.0000000000000002 ***
## short_listTRUE -0.0002233  0.0004601  -0.485               0.628    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 61:
## 
## Coefficients:
##                 Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)    0.0112650  0.0001136   99.15 <0.0000000000000002 ***
## short_listTRUE 0.0019039  0.0001402   13.58 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 62:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0104588  0.0003939  26.554 <0.0000000000000002 ***
## short_listTRUE -0.0010404  0.0004713  -2.208              0.0273 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 63:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0088039  0.0003593  24.500 < 0.0000000000000002 ***
## short_listTRUE 0.0020095  0.0004632   4.338            0.0000144 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 64:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0207843  0.0002282  91.080 < 0.0000000000000002 ***
## short_listTRUE 0.0017465  0.0003042   5.742        0.00000000942 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 65:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0136841  0.0003857  35.481 < 0.0000000000000002 ***
## short_listTRUE -0.0017372  0.0004613  -3.766             0.000166 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 66:
## 
## Coefficients:
##                 Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)    0.0132102  0.0004836  27.317 < 0.0000000000000002 ***
## short_listTRUE 0.0037765  0.0005855   6.451       0.000000000112 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 67:
## 
## Coefficients:
##                  Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)     0.0219813  0.0003095  71.033 < 0.0000000000000002 ***
## short_listTRUE -0.0011535  0.0003847  -2.999              0.00271 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 68:
## 
## Coefficients:
##                  Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)     0.0193964  0.0001788   108.5 <0.0000000000000002 ***
## short_listTRUE -0.0026870  0.0002297   -11.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Topic 69:
## 
## Coefficients:
##                   Estimate  Std. Error t value            Pr(>|t|)    
## (Intercept)     0.00275135  0.00001988 138.408 <0.0000000000000002 ***
## short_listTRUE -0.00002552  0.00002595  -0.983               0.325    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

References

Airoldi, E. M., & Bischof, J. M. (2016). Improving and Evaluating Topic Models and Other Models of Text. Journal of the American Statistical Association, 111(516), 1381–1403. https://doi.org/10.1080/01621459.2015.1051182

Andeweg, R. B., & Thomassen, J. J. (2005). Modes of Political Representation: Toward a New Typology. Legislative Studies Quarterly, 30(4), 507–528. https://doi.org/10.3162/036298005X201653

Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., … Zhu, M. (2013). A Practical Algorithm for Topic Modeling with Provable Guarantees. In S. Dasgupta & D. McAllester (Eds.), Proceedings of the 30th International Conference on Machine Learning (Vol. 28, pp. 280–288). Atlanta, Georgia, USA: PMLR. Retrieved from http://proceedings.mlr.press/v28/arora13.pdf

Audickas, L., Hawkins, O., & Cracknell, R. (2017). UK Election Statistics: 1918-2017 (Briefing Paper No. CBP7529) (p. 89). London: House of Commons Library. Retrieved from http://researchbriefings.parliament.uk/ResearchBriefing/Summary/CBP-7529

Benoit, K. (2018). Quanteda: Quantitative Analysis of Textual Data. https://doi.org/10.5281/zenodo.1004683

Benoit, K., & Matsuo, A. (2018). Spacyr: Wrapper to the ’spaCy’ ’NLP’ Library. Retrieved from http://github.com/quanteda/spacyr

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.

Bligh, M., Merolla, J., Schroedel, J. R., & Gonzalez, R. (2010). Finding Her Voice: Hillary Clinton’s Rhetoric in the 2008 Presidential Campaign. Women’s Studies, 39(8), 823–850. https://doi.org/10.1080/00497878.2010.513316

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed). Hillsdale, N.J: L. Erlbaum Associates.

Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21(11), 1129–1164. https://doi.org/10.1002/spe.4380211102

Gagolewski, M. (2018). R package stringi: Character string processing facilities. https://doi.org/10.5281/zenodo.1292492

Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(03), 267–297. https://doi.org/10.1093/pan/mps028

Honnibal, M., & Montani, I. (2017). spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. To Appear. Retrieved from https://spacy.io

Jones, J. J. (2016). Talk "Like a Man": The Linguistic Styles of Hillary Clinton, 1992-2013. Perspectives on Politics, 14(03), 625–642. https://doi.org/10.1017/S1537592716001092

Kelly, R. (2016). All-women shortlists (Briefing Paper No. 5057) (p. 34). London: House of Commons Library. Retrieved from https://researchbriefings.parliament.uk/ResearchBriefing/Summary/SN05057

Kincaid, J. P., Fishburne, R. P., Rogers, R. L., & Chissom, B. S. (1975). Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel: Fort Belvoir, VA: Defense Technical Information Center. https://doi.org/10.21236/ADA006655

Lee, M., & Mimno, D. (2014). Low-dimensional Embeddings for Interpretable Anchor-based Topic Inference. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 1319–1328). Doha, Qatar: Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1138

Newman, M. L., Groom, C. J., Handelman, L. D., & Pennebaker, J. W. (2008). Gender Differences in Language Use: An Analysis of 14,000 Text Samples. Discourse Processes, 45(3), 211–236. https://doi.org/10.1080/01638530802073712

Odell, E. (2018). Hansard Speeches and Sentiment V2.5.1 [dataset]. https://doi.org/10.5281/zenodo.1306964

Pennebaker, J. W., Boyd, R. L., Jordan, K., & Blackburn, K. (2015). The Development and Psychometric Properties of LIWC2015, 26. Retrieved from https://repositories.lib.utexas.edu/bitstream/handle/2152/31333/LIWC2015_LanguageManual.pdf

Pitkin, H. F. (1967). The concept of representation (1. paperback ed., [Nachdr.]). Berkeley, Calif.: Univ. of California Press.

Roberts, M. E., Stewart, B. M., & Airoldi, E. M. (2016). A Model of Text for Experimentation in the Social Sciences. Journal of the American Statistical Association, 111(515), 988–1003. https://doi.org/10.1080/01621459.2016.1141684

Roberts, M. E., Stewart, B. M., & Tingley, D. (2018). Stm: R Package for Structural Topic Models. Retrieved from http://www.structuraltopicmodel.com

Yu, B. (2014). Language and gender in Congressional speech. Literary and Linguistic Computing, 29(1), 118–132. https://doi.org/10.1093/llc/fqs073


  1. e.g. a reference to “the member for Bethnal Green and Bow” in keeping with Parliamentary convention of identifying MPs by their seat rather than their name would be followed by “(Rushnara Ali)”.

  2. Special Educational Needs